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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-11-14
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">167</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">20</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
                    <span id="avg-score" class="font-semibold text-secondary">2.5</span>
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                <span id="display-count" class="font-medium">显示 167 篇论文 (共 167 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10138v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>GPR：面向大规模广告推荐的一种生成式预训练单模型范式
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>10/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            GPR: Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jun Zhang, Yi Li, Yue Liu, Changping Wang, Yuan Wang, Yuling Xiong, Xun Liu, Hai...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究传统多阶段广告推荐系统的目标不一致和误差传播问题，核心思想是将广告推荐重新定义为端到端生成任务，通过统一表示、异构分层解码器和多阶段联合训练策略构建单一模型框架。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对广告推荐系统提出统一的生成式预训练模型范式，完全契合核心领域进展和直接LLM应用两大焦点，实现了端到端的生成任务重构。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:50:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10138v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10138v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As an intelligent infrastructure connecting users with commercial content, advertising recommendation systems play a central role in information flow and value creation within the digital economy. However, existing multi-stage advertising recommendation systems suffer from objective misalignment and error propagation, making it difficult to achieve global optimality, while unified generative recommendation models still struggle to meet the demands of practical industrial applications. To address these issues, we propose GPR (Generative Pre-trained Recommender), the first one-model framework that redefines advertising recommendation as an end-to-end generative task, replacing the traditional cascading paradigm with a unified generative approach. To realize GPR, we introduce three key innovations spanning unified representation, network architecture, and training strategy. First, we design a unified input schema and tokenization method tailored to advertising scenarios, mapping both ads and organic content into a shared multi-level semantic ID space, thereby enhancing semantic alignment and modeling consistency across heterogeneous data. Second, we develop the Heterogeneous Hierarchical Decoder (HHD), a dual-decoder architecture that decouples user intent modeling from ad generation, achieving a balance between training efficiency and inference flexibility while maintaining strong modeling capacity. Finally, we propose a multi-stage joint training strategy that integrates Multi-Token Prediction (MTP), Value-Aware Fine-Tuning and the Hierarchy Enhanced Policy Optimization (HEPO) algorithm, forming a complete generative recommendation pipeline that unifies interest modeling, value alignment, and policy optimization. GPR has been fully deployed in the Tencent Weixin Channels advertising system, delivering significant improvements in key business metrics including GMV and CTCVR.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10492v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>不要浪费它：通过多头解码利用结构化人类先验指导生成式推荐系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunkai Zhang, Qiang Zhang, Feng, Lin, Ruizhong Qiu, Hanchao Yu, Jason Liu, Yingl...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何在生成式推荐系统中有效利用结构化人类先验知识（如物品分类、时序模式）的问题；核心方法是提出轻量级、先验条件化的适配器头，通过多头解码策略将用户意图沿人类可理解维度进行解耦，并引入分层组合策略建模不同先验类型间的复杂交互。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对生成式推荐系统，提出了整合结构化人类先验知识的多头解码框架，与核心领域进展和直接LLM应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:59:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10492v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10492v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Optimizing recommender systems for objectives beyond accuracy, such as diversity, novelty, and personalization, is crucial for long-term user satisfaction. To this end, industrial practitioners have accumulated vast amounts of structured domain knowledge, which we term human priors (e.g., item taxonomies, temporal patterns). This knowledge is typically applied through post-hoc adjustments during ranking or post-ranking. However, this approach remains decoupled from the core model learning, which is particularly undesirable as the industry shifts to end-to-end generative recommendation foundation models. On the other hand, many methods targeting these beyond-accuracy objectives often require architecture-specific modifications and discard these valuable human priors by learning user intent in a fully unsupervised manner. Instead of discarding the human priors accumulated over years of practice, we introduce a backbone-agnostic framework that seamlessly integrates these human priors directly into the end-to-end training of generative recommenders. With lightweight, prior-conditioned adapter heads inspired by efficient LLM decoding strategies, our approach guides the model to disentangle user intent along human-understandable axes (e.g., interaction types, long- vs. short-term interests). We also introduce a hierarchical composition strategy for modeling complex interactions across different prior types. Extensive experiments on three large-scale datasets demonstrate that our method significantly enhances both accuracy and beyond-accuracy objectives. We also show that human priors allow the backbone model to more effectively leverage longer context lengths and larger model sizes.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09873v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>HierRouter：通过强化学习对专业化大语言模型进行协同路由
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            HierRouter: Coordinated Routing of Specialized Large Language Models via Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nikunj Gupta, Bill Guo, Rajgopal Kannan, Viktor K. Prasanna
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何降低大型语言模型在资源受限环境中的计算成本问题，核心思想是采用分层路由机制，通过强化学习智能体动态组装轻量级专业模型的推理流水线，实现上下文感知的多跳推理路径选择。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过强化学习协调多个轻量级专业LLM的推理路径，直接解决了LLM在资源受限环境中的部署效率问题，与Enabling LLM Tech和Direct LLM Applications高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:12:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09873v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09873v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) deliver state-of-the-art performance across many tasks but impose high computational and memory costs, limiting their deployment in resource-constrained or real-time settings. To address this, we propose HierRouter, a hierarchical routing approach that dynamically assembles inference pipelines from a pool of specialized, lightweight language models. Formulated as a finite-horizon Markov Decision Process (MDP), our approach trains a Proximal Policy Optimization (PPO)-based reinforcement learning agent to iteratively select which models to invoke at each stage of multi-hop inference. The agent conditions on the evolving context and accumulated cost to make context-aware routing decisions. Experiments with three open-source candidate LLMs across six benchmarks, including QA, code generation, and mathematical reasoning, show that HierRouter improves response quality by up to 2.4x compared to using individual models independently, while incurring only a minimal additional inference cost on average. These results highlight the promise of hierarchical routing for cost-efficient, high-performance LLM inference. All codes can be found here https://github.com/ Nikunj-Gupta/hierouter.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09980v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>为动态RAG中的及时检索建模不确定性趋势
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bo Li, Tian Tian, Zhenghua Xu, Hao Cheng, Shikun Zhang, Wei Ye
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究动态检索增强生成中确定最佳检索时机的问题，核心思想是通过建模token级不确定性的动态趋势（利用熵序列的一阶和二阶差分）来检测新兴不确定性，实现更早更精确的检索触发。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接解决动态RAG中的检索时机决策问题，这是搜索和推荐系统中LLM应用的核心挑战，其不确定性趋势建模方法具有通用性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 05:28:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09980v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09980v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Dynamic retrieval-augmented generation (RAG) allows large language models (LLMs) to fetch external knowledge on demand, offering greater adaptability than static RAG. A central challenge in this setting lies in determining the optimal timing for retrieval. Existing methods often trigger retrieval based on low token-level confidence, which may lead to delayed intervention after errors have already propagated. We introduce Entropy-Trend Constraint (ETC), a training-free method that determines optimal retrieval timing by modeling the dynamics of token-level uncertainty. Specifically, ETC utilizes first- and second-order differences of the entropy sequence to detect emerging uncertainty trends, enabling earlier and more precise retrieval. Experiments on six QA benchmarks with three LLM backbones demonstrate that ETC consistently outperforms strong baselines while reducing retrieval frequency. ETC is particularly effective in domain-specific scenarios, exhibiting robust generalization capabilities. Ablation studies and qualitative analyses further confirm that trend-aware uncertainty modeling yields more effective retrieval timing. The method is plug-and-play, model-agnostic, and readily integrable into existing decoding pipelines. Implementation code is included in the supplementary materials.
                </div>
            </details>
    </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09865v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>令牌内理性优化：通过自反馈实现准确且简洁的大语言模型推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingye Zhu, Yi Liu, Zheren Fu, Quan Wang, Yongdong Zhang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM链式推理训练中的泛化问题，核心思想是引入校正因子和自反馈机制，在单次前向传播中实现token级探索和理性优化，从而生成准确简洁的推理路径。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的InTRO框架通过token级探索和自反馈机制优化推理过程，其核心思想可迁移到推荐系统的序列建模和决策优化中，对提升模型推理效率和准确性有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 01:47:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09865v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09865v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Training Large Language Models (LLMs) for chain-of-thought reasoning presents a significant challenge: supervised fine-tuning on a single "golden" rationale hurts generalization as it penalizes equally valid alternatives, whereas reinforcement learning with verifiable rewards struggles with credit assignment and prohibitive computational cost. To tackle these limitations, we introduce InTRO (In-Token Rationality Optimization), a new framework that enables both token-level exploration and self-feedback for accurate and concise reasoning. Instead of directly optimizing an intractable objective over all valid reasoning paths, InTRO leverages correction factors-token-wise importance weights estimated by the information discrepancy between the generative policy and its answer-conditioned counterpart, for informative next token selection. This approach allows the model to perform token-level exploration and receive self-generated feedback within a single forward pass, ultimately encouraging accurate and concise rationales. Across six math-reasoning benchmarks, InTRO consistently outperforms other baselines, raising solution accuracy by up to 20% relative to the base model. Its chains of thought are also notably more concise, exhibiting reduced verbosity. Beyond this, InTRO enables cross-domain transfer, successfully adapting to out-of-domain reasoning tasks that extend beyond the realm of mathematics, demonstrating robust generalization.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10465v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>超越启发式：面向知识密集型任务的基于供给的提示优化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Beyond Elicitation: Provision-based Prompt Optimization for Knowledge-Intensive Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunzhe Xu, Zhuosheng Zhang, Zhe Liu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究知识密集型任务中传统提示优化的局限性，核心思想是将提示优化重新定义为系统性知识集成而非能力激发，通过知识缺口填补、批量评估和自适应剪枝机制实现精准知识注入。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出基于知识供给的提示优化框架，直接针对知识密集型任务的LLM应用瓶颈，与推荐搜索系统中处理专业领域知识的需求高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:33:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10465v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10465v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While prompt optimization has emerged as a critical technique for enhancing language model performance, existing approaches primarily focus on elicitation-based strategies that search for optimal prompts to activate models' capabilities. These methods exhibit fundamental limitations when addressing knowledge-intensive tasks, as they operate within fixed parametric boundaries rather than providing the factual knowledge, terminology precision, and reasoning patterns required in specialized domains. To address these limitations, we propose Knowledge-Provision-based Prompt Optimization (KPPO), a framework that reformulates prompt optimization as systematic knowledge integration rather than potential elicitation. KPPO introduces three key innovations: 1) a knowledge gap filling mechanism for knowledge gap identification and targeted remediation; 2) a batch-wise candidate evaluation approach that considers both performance improvement and distributional stability; 3) an adaptive knowledge pruning strategy that balances performance and token efficiency, reducing up to 29% token usage. Extensive evaluation on 15 knowledge-intensive benchmarks from various domains demonstrates KPPO's superiority over elicitation-based methods, with an average performance improvement of ~6% over the strongest baseline while achieving comparable or lower token consumption. Code at: https://github.com/xyz9911/KPPO.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10029v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ScaleFormer：面向长上下文Transformer的跨度表示累积
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ScaleFormer: Span Representation Cumulation for Long-Context Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiangshu Du, Wenpeng Yin, Philip Yu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何解决标准自注意力机制二次复杂度限制Transformer处理长上下文任务的问题，其核心思想是通过将长输入分段为重叠块，并利用参数无关的融合机制为每个块生成具有文档结构位置感知的压缩表示，通过累积前后块上下文向量来捕捉文档叙事流。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的长上下文Transformer优化方法直接属于Transformer架构效率提升领域，其参数无关的融合机制对处理长序列推荐和搜索任务具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:05:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10029v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10029v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The quadratic complexity of standard self-attention severely limits the application of Transformer-based models to long-context tasks. While efficient Transformer variants exist, they often require architectural changes and costly pre-training from scratch. To circumvent this, we propose ScaleFormer(Span Representation Cumulation for Long-Context Transformer) - a simple and effective plug-and-play framework that adapts off-the-shelf pre-trained encoder-decoder models to process long sequences without requiring architectural modifications. Our approach segments long inputs into overlapping chunks and generates a compressed, context-aware representation for the decoder. The core of our method is a novel, parameter-free fusion mechanism that endows each chunk's representation with structural awareness of its position within the document. It achieves this by enriching each chunk's boundary representations with cumulative context vectors from all preceding and succeeding chunks. This strategy provides the model with a strong signal of the document's narrative flow, achieves linear complexity, and enables pre-trained models to reason effectively over long-form text. Experiments on long-document summarization show that our method is highly competitive with and often outperforms state-of-the-art approaches without requiring architectural modifications or external retrieval mechanisms.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09984v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>多语言检索增强生成中的语言漂移：特征描述与解码时缓解策略
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Language Drift in Multilingual Retrieval-Augmented Generation: Characterization and Decoding-Time Mitigation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bo Li, Zhenghua Xu, Rui Xie
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多语言检索增强生成中因检索证据与查询语言不一致导致的语言漂移现象，核心发现是语言漂移源于解码器崩溃和英语作为语义吸引器，并提出通过软约束解码在生成时惩罚非目标语言令牌来缓解此问题。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究多语言RAG系统中的语言漂移问题，并提出解码时缓解策略，对搜索和推荐系统中的多语言内容生成具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 05:36:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09984v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09984v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multilingual Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to perform knowledge-intensive tasks in multilingual settings by leveraging retrieved documents as external evidence. However, when the retrieved evidence differs in language from the user query and in-context exemplars, the model often exhibits language drift by generating responses in an unintended language. This phenomenon is especially pronounced during reasoning-intensive decoding, such as Chain-of-Thought (CoT) generation, where intermediate steps introduce further language instability. In this paper, we systematically study output language drift in multilingual RAG across multiple datasets, languages, and LLM backbones. Our controlled experiments reveal that the drift results not from comprehension failure but from decoder-level collapse, where dominant token distributions and high-frequency English patterns dominate the intended generation language. We further observe that English serves as a semantic attractor under cross-lingual conditions, emerging as both the strongest interference source and the most frequent fallback language. To mitigate this, we propose Soft Constrained Decoding (SCD), a lightweight, training-free decoding strategy that gently steers generation toward the target language by penalizing non-target-language tokens. SCD is model-agnostic and can be applied to any generation algorithm without modifying the architecture or requiring additional data. Experiments across three multilingual datasets and multiple typologically diverse languages show that SCD consistently improves language alignment and task performance, providing an effective and generalizable solution in multilingual RAG.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10453v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>针对模糊请求的意图推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Reasoning About Intent for Ambiguous Requests
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Irina Saparina, Mirella Lapata
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM处理模糊请求时的意图误解问题，核心方法是生成多个解释-答案对的结构化响应，通过强化学习和定制奖励函数训练模型覆盖多种有效解释。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对LLM处理模糊请求的核心问题，提出多解释-答案对的结构化响应方法，直接适用于搜索和推荐系统的意图理解场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:18:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10453v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10453v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models often respond to ambiguous requests by implicitly committing to one interpretation. Intent misunderstandings can frustrate users and create safety risks. To address this, we propose generating multiple interpretation-answer pairs in a single structured response to ambiguous requests. Our models are trained with reinforcement learning and customized reward functions using multiple valid answers as supervision. Experiments on conversational question answering and semantic parsing demonstrate that our method achieves higher coverage of valid answers than baseline approaches. Human evaluation confirms that predicted interpretations are highly aligned with their answers. Our approach promotes transparency with explicit interpretations, achieves efficiency by requiring only one generation step, and supports downstream applications through its structured output format.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10552v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>URaG：多模态大语言模型中的统一检索与生成，用于高效长文档理解
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            URaG: Unified Retrieval and Generation in Multimodal LLMs for Efficient Long Document Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongxin Shi, Jiapeng Wang, Zeyu Shan, Dezhi Peng, Zening Lin, Lianwen Jin
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多模态大模型处理长文档时的信息干扰和计算效率问题，核心思想是利用Transformer不同层级的注意力模式，将早期层转换为轻量级检索模块来筛选相关页面，实现检索与生成的统一端到端优化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出在Transformer架构中统一检索与生成，直接针对计算效率问题，与推荐搜索系统的架构优化需求高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:54:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10552v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10552v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent multimodal large language models (MLLMs) still struggle with long document understanding due to two fundamental challenges: information interference from abundant irrelevant content, and the quadratic computational cost of Transformer-based architectures. Existing approaches primarily fall into two categories: token compression, which sacrifices fine-grained details; and introducing external retrievers, which increase system complexity and prevent end-to-end optimization. To address these issues, we conduct an in-depth analysis and observe that MLLMs exhibit a human-like coarse-to-fine reasoning pattern: early Transformer layers attend broadly across the document, while deeper layers focus on relevant evidence pages. Motivated by this insight, we posit that the inherent evidence localization capabilities of MLLMs can be explicitly leveraged to perform retrieval during the reasoning process, facilitating efficient long document understanding. To this end, we propose URaG, a simple-yet-effective framework that Unifies Retrieval and Generation within a single MLLM. URaG introduces a lightweight cross-modal retrieval module that converts the early Transformer layers into an efficient evidence selector, identifying and preserving the most relevant pages while discarding irrelevant content. This design enables the deeper layers to concentrate computational resources on pertinent information, improving both accuracy and efficiency. Extensive experiments demonstrate that URaG achieves state-of-the-art performance while reducing computational overhead by 44-56%. The code is available at https://github.com/shi-yx/URaG.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10643v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>大型语言模型的黑盒在线策略蒸馏
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Black-Box On-Policy Distillation of Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianzhu Ye, Li Dong, Zewen Chi, Xun Wu, Shaohan Huang, Furu Wei
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究黑盒环境下大语言模型的蒸馏问题，核心思想是通过生成对抗框架将学生模型作为生成器，训练判别器区分师生模型输出，实现无需内部参数的在线蒸馏。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的黑盒蒸馏方法直接适用于推荐系统和广告领域的模型优化，生成对抗蒸馏框架为LLM在工业场景的应用提供了高效训练范式。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:58:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10643v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10643v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Black-box distillation creates student large language models (LLMs) by learning from a proprietary teacher model's text outputs alone, without access to its internal logits or parameters. In this work, we introduce Generative Adversarial Distillation (GAD), which enables on-policy and black-box distillation. GAD frames the student LLM as a generator and trains a discriminator to distinguish its responses from the teacher LLM's, creating a minimax game. The discriminator acts as an on-policy reward model that co-evolves with the student, providing stable, adaptive feedback. Experimental results show that GAD consistently surpasses the commonly used sequence-level knowledge distillation. In particular, Qwen2.5-14B-Instruct (student) trained with GAD becomes comparable to its teacher, GPT-5-Chat, on the LMSYS-Chat automatic evaluation. The results establish GAD as a promising and effective paradigm for black-box LLM distillation.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10566v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>层归一化对Transformer记忆与泛化能力的影响
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Impact of Layer Norm on Memorization and Generalization in Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rishi Singhal, Jung-Eun Kim
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LayerNorm在Pre-LayerNorm和Post-LayerNorm两种Transformer架构中对记忆化和学习过程的影响机制。核心发现是LayerNorm在Pre-LayerNorm架构中主要确保学习稳定性，而在Post-LayerNorm架构中则显著影响记忆化行为，移除其参数会分别加剧或缓解记忆化现象。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文深入分析LayerNorm在Transformer架构中对记忆化和泛化的影响机制，直接关联Transformer架构优化和核心组件作用机理，对推荐和搜索系统的模型稳定性与泛化能力具有重要指导意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:07:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10566v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10566v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Layer Normalization (LayerNorm) is one of the fundamental components in transformers that stabilizes training and improves optimization. In recent times, Pre-LayerNorm transformers have become the preferred choice over Post-LayerNorm transformers due to their stable gradient flow. However, the impact of LayerNorm on learning and memorization across these architectures remains unclear. In this work, we investigate how LayerNorm influences memorization and learning for Pre- and Post-LayerNorm transformers. We identify that LayerNorm serves as a key factor for stable learning in Pre-LayerNorm transformers, while in Post-LayerNorm transformers, it impacts memorization. Our analysis reveals that eliminating LayerNorm parameters in Pre-LayerNorm models exacerbates memorization and destabilizes learning, while in Post-LayerNorm models, it effectively mitigates memorization by restoring genuine labels. We further precisely identify that early layers LayerNorm are the most critical over middle/later layers and their influence varies across Pre and Post LayerNorm models. We have validated it through 13 models across 6 Vision and Language datasets. These insights shed new light on the role of LayerNorm in shaping memorization and learning in transformers.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10488v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SPOT：基于视觉Transformer中令牌相关性的注意力动态稀疏化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SPOT: Sparsification with Attention Dynamics via Token Relevance in Vision Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Oded Schlesinger, Amirhossein Farzam, J. Matias Di Martino, Guillermo Sapiro
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究Vision Transformers中计算复杂度随token数量平方增长的问题，核心思想是利用token嵌入、交互和跨层注意力动态来推断token重要性，实现早期检测和消除冗余token的上下文感知稀疏化框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的基于注意力动态的token稀疏化方法直接关联Transformer架构效率优化，其token重要性评估和动态剪枝机制对推荐系统中处理长序列和复杂特征具有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:56:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10488v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10488v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">eess.IV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While Vision Transformers (ViT) have demonstrated remarkable performance across diverse tasks, their computational demands are substantial, scaling quadratically with the number of processed tokens. Compact attention representations, reflecting token interaction distributions, can guide early detection and reduction of less salient tokens prior to attention computation. Motivated by this, we present SParsification with attentiOn dynamics via Token relevance (SPOT), a framework for early detection of redundant tokens within ViTs that leverages token embeddings, interactions, and attention dynamics across layers to infer token importance, resulting in a more context-aware and interpretable relevance detection process. SPOT informs token sparsification and facilitates the elimination of such tokens, improving computational efficiency without sacrificing performance. SPOT employs computationally lightweight predictors that can be plugged into various ViT architectures and learn to derive effective input-specific token prioritization across layers. Its versatile design supports a range of performance levels adaptable to varying resource constraints. Empirical evaluations demonstrate significant efficiency gains of up to 40% compared to standard ViTs, while maintaining or even improving accuracy. Code and models are available at https://github.com/odedsc/SPOT .
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10004v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>LampQ：面向视觉Transformer的精确层混合精度量化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minjun Kim, Jaeri Lee, Jongjin Kim, Jeongin Yun, Yongmo Kwon, U Kang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何准确量化预训练视觉Transformer模型，核心思想是提出层级混合精度量化方法，通过类型感知的Fisher敏感度度量和整数线性规划优化，为不同敏感度的Transformer组件分配最优位宽。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于Transformer架构的效率优化和量化技术，虽然针对视觉Transformer，但其层级混合精度量化的核心方法可直接应用于推荐和搜索系统中的Transformer模型优化。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:12:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10004v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10004v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    How can we accurately quantize a pre-trained Vision Transformer model? Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computation demands with minimal accuracy degradation. However, existing methods rely on uniform precision, ignoring the diverse sensitivity of ViT components to quantization. Metric-based Mixed Precision Quantization (MPQ) is a promising alternative, but previous MPQ methods for ViTs suffer from three major limitations: 1) coarse granularity, 2) mismatch in metric scale across component types, and 3) quantization-unaware bit allocation. In this paper, we propose LampQ (Layer-wise Mixed Precision Quantization for Vision Transformers), an accurate metric-based MPQ method for ViTs to overcome these limitations. LampQ performs layer-wise quantization to achieve both fine-grained control and efficient acceleration, incorporating a type-aware Fisher-based metric to measure sensitivity. Then, LampQ assigns bit-widths optimally through integer linear programming and further updates them iteratively. Extensive experiments show that LampQ provides the state-of-the-art performance in quantizing ViTs pre-trained on various tasks such as image classification, object detection, and zero-shot quantization.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10075v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>格式至关重要：多模态大语言模型在审查表格和图表证据方面的鲁棒性
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Format Matters: The Robustness of Multimodal LLMs in Reviewing Evidence from Tables and Charts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xanh Ho, Yun-Ang Wu, Sunisth Kumar, Florian Boudin, Atsuhiro Takasu, Akiko Aizaw...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态大语言模型在不同格式证据（表格与图表）下验证科学主张的鲁棒性问题，核心发现是模型在表格证据上表现更好而在图表证据上存在困难，揭示了当前多模态模型在跨模态泛化能力上的局限性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接评估多模态LLM在不同数据格式下的推理能力，与VLM异构数据处理和LLM核心技术进步高度相关，但未直接应用于推荐或搜索场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:29:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10075v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10075v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the growing number of submitted scientific papers, there is an increasing demand for systems that can assist reviewers in evaluating research claims. Experimental results are a core component of scientific work, often presented in varying formats such as tables or charts. Understanding how robust current multimodal large language models (multimodal LLMs) are at verifying scientific claims across different evidence formats remains an important and underexplored challenge. In this paper, we design and conduct a series of experiments to assess the ability of multimodal LLMs to verify scientific claims using both tables and charts as evidence. To enable this evaluation, we adapt two existing datasets of scientific papers by incorporating annotations and structures necessary for a multimodal claim verification task. Using this adapted dataset, we evaluate 12 multimodal LLMs and find that current models perform better with table-based evidence while struggling with chart-based evidence. We further conduct human evaluations and observe that humans maintain strong performance across both formats, unlike the models. Our analysis also reveals that smaller multimodal LLMs (under 8B) show weak correlation in performance between table-based and chart-based tasks, indicating limited cross-modal generalization. These findings highlight a critical gap in current models' multimodal reasoning capabilities. We suggest that future multimodal LLMs should place greater emphasis on improving chart understanding to better support scientific claim verification.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10395v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>AgentEvolver：面向高效自进化智能体系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AgentEvolver: Towards Efficient Self-Evolving Agent System
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunpeng Zhai, Shuchang Tao, Cheng Chen, Anni Zou, Ziqian Chen, Qingxu Fu, Shinji...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何解决基于LLM的自主智能体开发成本高、探索效率低的问题，核心思想是利用LLM的语义理解和推理能力，通过自我提问生成任务、自我导航重用经验、自我归因分配奖励三种协同机制，实现智能体的持续自我进化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出利用LLM语义理解能力构建自进化智能体系统，通过自我提问、导航和归因机制提升探索效率，直接应用于需要高效自主学习的推荐和搜索场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:14:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10395v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10395v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in diverse environments. However, current approaches to developing such agents remain costly and inefficient, as they typically require manually constructed task datasets and reinforcement learning (RL) pipelines with extensive random exploration. These limitations lead to prohibitively high data-construction costs, low exploration efficiency, and poor sample utilization. To address these challenges, we present AgentEvolver, a self-evolving agent system that leverages the semantic understanding and reasoning capabilities of LLMs to drive autonomous agent learning. AgentEvolver introduces three synergistic mechanisms: (i) self-questioning, which enables curiosity-driven task generation in novel environments, reducing dependence on handcrafted datasets; (ii) self-navigating, which improves exploration efficiency through experience reuse and hybrid policy guidance; and (iii) self-attributing, which enhances sample efficiency by assigning differentiated rewards to trajectory states and actions based on their contribution. By integrating these mechanisms into a unified framework, AgentEvolver enables scalable, cost-effective, and continual improvement of agent capabilities. Preliminary experiments indicate that AgentEvolver achieves more efficient exploration, better sample utilization, and faster adaptation compared to traditional RL-based baselines.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10457v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>探索大型语言模型的状态跟踪能力
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Exploring State Tracking Capabilities of Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kiamehr Rezaee, Jose Camacho-Collados, Mohammad Taher Pilehvar
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究大型语言模型在状态跟踪问题上的能力，即模型需要持续追踪多个实体的状态演变。核心发现是新一代LLM具备状态跟踪能力，特别是结合思维链等机制时，而旧模型在初始阶段能理解任务但会在多步后失败。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探索LLM的状态跟踪能力，这是推荐系统和搜索中用户状态建模的核心技术，直接关联到LLM在复杂交互场景中的应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:25:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10457v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10457v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have demonstrated impressive capabilities in solving complex tasks, including those requiring a certain level of reasoning. In this paper, we focus on state tracking, a problem where models need to keep track of the state governing a number of entities. To isolate the state tracking component from other factors, we propose a benchmark based on three well-defined state tracking tasks and analyse the performance of LLMs in different scenarios. The results indicate that the recent generation of LLMs (specifically, GPT-4 and Llama3) are capable of tracking state, especially when integrated with mechanisms such as Chain of Thought. However, models from the former generation, while understanding the task and being able to solve it at the initial stages, often fail at this task after a certain number of steps.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10645v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ParoQuant：用于高效推理大语言模型推理的成对旋转量化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yesheng Liang, Haisheng Chen, Song Han, Zhijian Liu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM权重量化中的异常值问题导致推理精度下降的核心问题，提出通过成对旋转量化和通道缩放来均衡通道幅度、缩小动态范围的核心方法。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM推理效率优化，属于核心LLM技术进展，对搜索和推荐系统的部署成本优化有直接价值。但其方法主要针对量化技术，与推荐系统核心算法创新的关联度中等。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:59:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10645v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10645v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Weight-only post-training quantization (PTQ) compresses the weights of Large Language Models (LLMs) into low-precision representations to reduce memory footprint and accelerate inference. However, the presence of outliers in weights and activations often leads to large quantization errors and severe accuracy degradation, especially in recent reasoning LLMs where errors accumulate across long chains of thought. Existing PTQ methods either fail to sufficiently suppress outliers or introduce significant overhead during inference. In this paper, we propose Pairwise Rotation Quantization (ParoQuant), a weight-only PTQ method that combines hardware-efficient and optimizable independent Givens rotations with channel-wise scaling to even out the magnitude across channels and narrow the dynamic range within each quantization group. We further co-design the inference kernel to fully exploit GPU parallelism and keep the rotations and scaling lightweight at runtime. ParoQuant achieves an average 2.4% accuracy improvement over AWQ on reasoning tasks with less than 10% overhead. This paves the way for more efficient and accurate deployment of reasoning LLMs.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10628v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Instella：具有卓越性能的完全开放语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Instella: Fully Open Language Models with Stellar Performance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiang Liu, Jialian Wu, Xiaodong Yu, Yusheng Su, Prakamya Mishra, Gowtham Ramesh,...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何构建完全开源的高性能语言模型以解决当前主流模型封闭限制透明度的问题，核心方法是使用公开数据和代码库训练3B参数模型家族，并通过指令调优和对齐技术开发通用及数学推理等专业变体。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要贡献是开发完全开源的3B参数语言模型Instella，虽然属于LLM基础技术进展，但未明确涉及推荐系统、搜索或广告的直接应用或架构创新。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:52:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10628v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10628v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
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                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially open, limiting transparency and reproducibility. In this work, we introduce Instella, a family of fully open three billion parameter language models trained entirely on openly available data and codebase. Powered by AMD Instinct MI300X GPUs, Instella is developed through large-scale pre-training, general-purpose instruction tuning, and alignment with human preferences. Despite using substantially fewer pre-training tokens than many contemporaries, Instella achieves state-of-the-art results among fully open models and is competitive with leading open-weight models of comparable size. We further release two specialized variants: Instella-Long, capable of handling context lengths up to 128K tokens, and Instella-Math, a reasoning-focused model enhanced through supervised fine-tuning and reinforcement learning on mathematical tasks. Together, these contributions establish Instella as a transparent, performant, and versatile alternative for the community, advancing the goal of open and reproducible language modeling research.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10297v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>局部混合检索增强文档问答
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Local Hybrid Retrieval-Augmented Document QA
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paolo Astrino
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何在保护数据隐私的前提下实现高质量的文档问答。核心方法是通过结合语义理解和关键词精度的混合检索策略，在本地基础设施上构建完整的问答系统，无需云端数据传输。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于检索增强的文档问答系统，与搜索领域直接相关，但其核心是隐私保护与本地部署的权衡问题，而非纯粹的检索算法创新。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:34:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10297v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10297v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Organizations handling sensitive documents face a critical dilemma: adopt cloud-based AI systems that offer powerful question-answering capabilities but compromise data privacy, or maintain local processing that ensures security but delivers poor accuracy. We present a question-answering system that resolves this trade-off by combining semantic understanding with keyword precision, operating entirely on local infrastructure without internet access. Our approach demonstrates that organizations can achieve competitive accuracy on complex queries across legal, scientific, and conversational documents while keeping all data on their machines. By balancing two complementary retrieval strategies and using consumer-grade hardware acceleration, the system delivers reliable answers with minimal errors, letting banks, hospitals, and law firms adopt conversational document AI without transmitting proprietary information to external providers. This work establishes that privacy and performance need not be mutually exclusive in enterprise AI deployment.
                </div>
            </details>
    </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09907v1" target="_blank" rel="noopener noreferrer">
                学习提出问题：面向大型推理模型的推理驱动与求解器自适应的数据合成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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        <div class="mb-2 text-base text-gray-700">
            Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis for Large Reasoning Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongxian Wei, Yilin Zhao, Li Shen, Xinrui Chen, Runxi Cheng, Sinan Du, Hao Yu, G...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注推理模型的数据合成方法，属于LLM核心技术的进展。推理能力在搜索系统中至关重要，可用于理解复杂查询意图和进行多步推理。这种数据合成技术可应用于构建更强大的搜索问答系统和推荐系统的推理组件。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:08:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09907v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09907v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing approaches face several challenges: (i) indiscriminate generation that ignores the solver's ability and yields low-value problems, or reliance on complex data pipelines to balance problem difficulty; and (ii) a lack of reasoning in problem generation, leading to shallow problem variants. In this paper, we develop a problem generator that reasons explicitly to plan problem directions before synthesis and adapts difficulty to the solver's ability. Specifically, we construct related problem pairs and augment them with intermediate problem-design CoT produced by a reasoning model. These data bootstrap problem-design strategies from the generator. Then, we treat the solver's feedback on synthetic problems as a reward signal, enabling the generator to calibrate difficulty and produce complementary problems near the edge of the solver's competence. Extensive experiments on 10 mathematical and general reasoning benchmarks show that our method achieves an average improvement of 2.5% and generalizes to both language and vision-language models. Moreover, a solver trained on the synthesized data provides improved rewards for continued generator training, enabling co-evolution and yielding a further 0.7% performance gain. Our code will be made publicly available here.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10301v1" target="_blank" rel="noopener noreferrer">
                重新思考多模态大语言模型中的视觉信息处理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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        <div class="mb-2 text-base text-gray-700">
            Rethinking Visual Information Processing in Multimodal LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongwan Kim, Viresh Ranjan, Takashi Nagata, Arnab Dhua, Amit Kumar K C
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多模态LLM中的视觉信息处理，这与VLM类比处理异构数据的理念相关，其中视觉和语言可被视为不同模态。虽然主要关注视觉模态，但其中关于多模态融合和处理的方法可能启发RecSys/Search/Ads中处理用户行为序列、上下文特征等异构数据的方式，实现更统一和高效的建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:36:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10301v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10301v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherently struggles to effectively integrate visual features due to the inherent mismatch between text and vision modalities. We tackle this issue from a novel perspective in which the LLM not only serves as a language model but also a powerful vision encoder. To this end, we present LLaViT - Large Language Models as extended Vision Transformers - which enables the LLM to simultaneously function as a vision encoder through three key modifications: (1) learning separate QKV projections for vision modality, (2) enabling bidirectional attention on visual tokens, and (3) incorporating both global and local visual representations. Through extensive controlled experiments on a wide range of LLMs, we demonstrate that LLaViT significantly outperforms the baseline LLaVA method on a multitude of benchmarks, even surpassing models with double its parameter count, establishing a more effective approach to vision-language modeling.
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            <a href="https://www.alphaxiv.org/abs/2511.10240v1" target="_blank" rel="noopener noreferrer">
                ProgRAG：基于知识图谱的抗幻觉渐进式检索与推理
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            <i class="fa fa-star mr-1"></i>4/10
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            ProgRAG: Hallucination-Resistant Progressive Retrieval and Reasoning over Knowledge Graphs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minbae Park, Hyemin Yang, Jeonghyun Kim, Kunsoo Park, Hyunjoon Kim
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注检索增强生成(RAG)中的幻觉问题，这属于纯粹的NLP评估基准范畴，与当前关注的推荐系统、搜索或广告核心技术进展无关。虽然知识图谱检索技术可能间接应用于搜索系统，但论文的核心焦点是解决LLM幻觉问题，而非直接提升RecSys/Search/Ads的排序或推荐性能。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:14:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10240v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10240v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) demonstrate strong reasoning capabilities but struggle with hallucinations and limited transparency. Recently, KG-enhanced LLMs that integrate knowledge graphs (KGs) have been shown to improve reasoning performance, particularly for complex, knowledge-intensive tasks. However, these methods still face significant challenges, including inaccurate retrieval and reasoning failures, often exacerbated by long input contexts that obscure relevant information or by context constructions that struggle to capture the richer logical directions required by different question types. Furthermore, many of these approaches rely on LLMs to directly retrieve evidence from KGs, and to self-assess the sufficiency of this evidence, which often results in premature or incorrect reasoning. To address the retrieval and reasoning failures, we propose ProgRAG, a multi-hop knowledge graph question answering (KGQA) framework that decomposes complex questions into sub-questions, and progressively extends partial reasoning paths by answering each sub-question. At each step, external retrievers gather candidate evidence, which is then refined through uncertainty-aware pruning by the LLM. Finally, the context for LLM reasoning is optimized by organizing and rearranging the partial reasoning paths obtained from the sub-question answers. Experiments on three well-known datasets demonstrate that ProgRAG outperforms existing baselines in multi-hop KGQA, offering improved reliability and reasoning quality.
                </div>
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 * @Date: 2025-10-09 23:23:38
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09966v1" target="_blank" rel="noopener noreferrer">
                REAP：通过递归评估和自适应规划增强RAG以实现多跳问答
            </a>
        </h3>
        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>4/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            REAP: Enhancing RAG with Recursive Evaluation and Adaptive Planning for Multi-Hop Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yijie Zhu, Haojie Zhou, Wanting Hong, Tailin Liu, Ning Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于RAG（检索增强生成）在多跳问答中的改进，这属于LLM在搜索领域的直接应用。虽然多跳问答与搜索相关，但该工作更侧重于问答系统而非推荐或广告中的核心排名问题，因此相关性中等。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:58:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09966v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09966v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-augmented generation (RAG) has been extensively employed to mitigate hallucinations in large language models (LLMs). However, existing methods for multi-hop reasoning tasks often lack global planning, increasing the risk of falling into local reasoning impasses. Insufficient exploitation of retrieved content and the neglect of latent clues fail to ensure the accuracy of reasoning outcomes. To overcome these limitations, we propose Recursive Evaluation and Adaptive Planning (REAP), whose core idea is to explicitly maintain structured sub-tasks and facts related to the current task through the Sub-task Planner (SP) and Fact Extractor (FE) modules. SP maintains a global perspective, guiding the overall reasoning direction and evaluating the task state based on the outcomes of FE, enabling dynamic optimization of the task-solving trajectory. FE performs fine-grained analysis over retrieved content to extract reliable answers and clues. These two modules incrementally enrich a logically coherent representation of global knowledge, enhancing the reliability and the traceability of the reasoning process. Furthermore, we propose a unified task paradigm design that enables effective multi-task fine-tuning, significantly enhancing SP's performance on complex, data-scarce tasks. We conduct extensive experiments on multiple public multi-hop datasets, and the results demonstrate that our method significantly outperforms existing RAG methods in both in-domain and out-of-domain settings, validating its effectiveness in complex multi-hop reasoning tasks.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.09935v1" target="_blank" rel="noopener noreferrer">
                利用大型语言模型识别知识组件
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Leveraging Large Language Models for Identifying Knowledge Components
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Canwen Wang, Jionghao Lin, Kenneth R. Koedinger
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用LLM进行知识组件识别，这属于教育技术或知识图谱领域的应用。虽然涉及LLM技术，但缺乏明确的搜索、推荐或广告领域的应用场景。核心LLM技术的潜在应用在推荐系统中不够具体和直接。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:00:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09935v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09935v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge Components (KCs) are foundational to adaptive learning systems, but their manual identification by domain experts is a significant bottleneck. While Large Language Models (LLMs) offer a promising avenue for automating this process, prior research has been limited to small datasets and has been shown to produce superfluous, redundant KC labels. This study addresses these limitations by first scaling a "simulated textbook" LLM prompting strategy (using GPT-4o-mini) to a larger dataset of 646 multiple-choice questions. We found that this initial automated approach performed significantly worse than an expert-designed KC model (RMSE 0.4285 vs. 0.4206) and generated an excessive number of KCs (569 vs. 101). To address the issue of redundancy, we proposed and evaluated a novel method for merging semantically similar KC labels based on their cosine similarity. This merging strategy significantly improved the model's performance; a model using a cosine similarity threshold of 0.8 achieved the best result, reducing the KC count to 428 and improving the RMSE to 0.4259. This demonstrates that while scaled LLM generation alone is insufficient, combining it with a semantic merging technique offers a viable path toward automating and refining KC identification.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10051v1" target="_blank" rel="noopener noreferrer">
                GraphIF：基于关系图提示增强大语言模型的多轮指令跟随能力
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            GraphIF: Enhancing Multi-Turn Instruction Following for Large Language Models with Relation Graph Prompt
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenhe Li, Can Lin, Ling Zheng, Wen-Da Wei, Junli Liang, Qi Song
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的指令跟随能力改进，属于核心LLM技术范畴。虽然多轮交互建模在推荐和搜索系统中具有潜在应用价值（如多轮对话推荐、复杂查询理解），但论文标题未明确体现与RecSys/Search/Ads的直接关联，且关系图提示技术在这些领域的应用路径不够清晰。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:49:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10051v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10051v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-turn instruction following is essential for building intelligent conversational systems that can consistently adhere to instructions across dialogue turns. However, existing approaches to enhancing multi-turn instruction following primarily rely on collecting or generating large-scale multi-turn dialogue datasets to fine-tune large language models (LLMs), which treat each response generation as an isolated task and fail to explicitly incorporate multi-turn instruction following into the optimization objectives. As a result, instruction-tuned LLMs often struggle with complex long-distance constraints. In multi-turn dialogues, relational constraints across turns can be naturally modeled as labeled directed edges, making graph structures particularly suitable for modeling multi-turn instruction following. Despite this potential, leveraging graph structures to enhance the multi-turn instruction following capabilities of LLMs remains unexplored. To bridge this gap, we propose GraphIF, a plug-and-play framework that models multi-turn dialogues as directed relation graphs and leverages graph prompts to enhance the instruction following capabilities of LLMs. GraphIF comprises three key components: (1) an agent-based relation extraction module that captures inter-turn semantic relations via action-triggered mechanisms to construct structured graphs; (2) a relation graph prompt generation module that converts structured graph information into natural language prompts; and (3) a response rewriting module that refines initial LLM outputs using the generated graph prompts. Extensive experiments on two long multi-turn dialogue datasets demonstrate that GraphIF can be seamlessly integrated into instruction-tuned LLMs and leads to significant improvements across all four multi-turn instruction-following evaluation metrics.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10577v1" target="_blank" rel="noopener noreferrer">
                DESS：DeBERTa增强的句法-语义方面情感三元组提取
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DESS: DeBERTa Enhanced Syntactic-Semantic Aspect Sentiment Triplet Extraction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vishal Thenuwara, Nisansa de Silva
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于情感分析中的三元组提取任务，属于NLP领域的细粒度情感分析技术。虽然DeBERTa是先进的Transformer模型，但该工作主要针对情感分析的具体应用，与推荐系统、搜索或广告的核心技术关联较弱。在推荐/搜索场景中，情感分析可能作为辅助特征，但并非核心排序或匹配算法的直接推动。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:16:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10577v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10577v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Fine-grained sentiment analysis faces ongoing challenges in Aspect Sentiment Triple Extraction (ASTE), particularly in accurately capturing the relationships between aspects, opinions, and sentiment polarities. While researchers have made progress using BERT and Graph Neural Networks, the full potential of advanced language models in understanding complex language patterns remains unexplored. We introduce DESS, a new approach that builds upon previous work by integrating DeBERTa's enhanced attention mechanism to better understand context and relationships in text. Our framework maintains a dual-channel structure, where DeBERTa works alongside an LSTM channel to process both meaning and grammatical patterns in text. We have carefully refined how these components work together, paying special attention to how different types of language information interact. When we tested DESS on standard datasets, it showed meaningful improvements over current methods, with F1-score increases of 4.85, 8.36, and 2.42 in identifying aspect opinion pairs and determining sentiment accurately. Looking deeper into the results, we found that DeBERTa's sophisticated attention system helps DESS handle complicated sentence structures better, especially when important words are far apart. Our findings suggest that upgrading to more advanced language models when thoughtfully integrated, can lead to real improvements in how well we can analyze sentiments in text. The implementation of our approach is publicly available at: https://github.com/VishalRepos/DESS.
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            <a href="https://www.alphaxiv.org/abs/2511.10182v1" target="_blank" rel="noopener noreferrer">
                超越黑盒：使用VISTA揭秘多轮大语言模型推理过程
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Beyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiran Zhang, Mingyang Lin, Mark Dras, Usman Naseem
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM推理过程的可解释性，这属于LLM内部工作机制分析而非核心推荐/搜索/广告领域的直接应用。虽然理解LLM推理可能间接帮助优化这些系统中的模型行为，但论文本身没有明确展示在推荐、搜索或广告中的具体应用潜力，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 10:48:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10182v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10182v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world problem-solving. However, analyzing the intricate reasoning processes within these interactions presents a significant challenge due to complex contextual dependencies and a lack of specialized visualization tools, leading to a high cognitive load for researchers. To address this gap, we present VISTA, an web-based Visual Interactive System for Textual Analytics in multi-turn reasoning tasks. VISTA allows users to visualize the influence of context on model decisions and interactively modify conversation histories to conduct "what-if" analyses across different models. Furthermore, the platform can automatically parse a session and generate a reasoning dependency tree, offering a transparent view of the model's step-by-step logical path. By providing a unified and interactive framework, VISTA significantly reduces the complexity of analyzing reasoning chains, thereby facilitating a deeper understanding of the capabilities and limitations of current LLMs. The platform is open-source and supports easy integration of custom benchmarks and local models.
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            <a href="https://www.alphaxiv.org/abs/2511.10229v1" target="_blank" rel="noopener noreferrer">
                LangGPS：语言可分性指导的联合多语言指令调优数据预选择
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LangGPS: Language Separability Guided Data Pre-Selection for Joint Multilingual Instruction Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Lei Huang, Weitao Ma, Qichen Hong, Yu...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多语言指令调优的数据选择方法，属于LLM训练优化技术。虽然数据选择策略可能间接影响多语言推荐/搜索模型的性能，但论文没有明确展示与推荐系统、搜索或广告的直接应用联系。该方法可能应用于多语言搜索系统的模型训练优化，但相关性较弱且间接。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:02:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10229v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10229v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Joint multilingual instruction tuning is a widely adopted approach to improve the multilingual instruction-following ability and downstream performance of large language models (LLMs), but the resulting multilingual capability remains highly sensitive to the composition and selection of the training data. Existing selection methods, often based on features like text quality, diversity, or task relevance, typically overlook the intrinsic linguistic structure of multilingual data. In this paper, we propose LangGPS, a lightweight two-stage pre-selection framework guided by language separability which quantifies how well samples in different languages can be distinguished in the model's representation space. LangGPS first filters training data based on separability scores and then refines the subset using existing selection methods. Extensive experiments across six benchmarks and 22 languages demonstrate that applying LangGPS on top of existing selection methods improves their effectiveness and generalizability in multilingual training, especially for understanding tasks and low-resource languages. Further analysis reveals that highly separable samples facilitate the formation of clearer language boundaries and support faster adaptation, while low-separability samples tend to function as bridges for cross-lingual alignment. Besides, we also find that language separability can serve as an effective signal for multilingual curriculum learning, where interleaving samples with diverse separability levels yields stable and generalizable gains. Together, we hope our work offers a new perspective on data utility in multilingual contexts and support the development of more linguistically informed LLMs.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10215v1" target="_blank" rel="noopener noreferrer">
                用于个性化对话生成的角色感知对齐框架
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Persona-Aware Alignment Framework for Personalized Dialogue Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guanrong Li, Xinyu Liu, Zhen Wu, Xinyu Dai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于个性化对话生成，属于纯LLM应用领域，与推荐系统、搜索或广告的核心技术进展无关。虽然个性化概念在推荐系统中很重要，但该框架针对的是对话生成任务，没有明确展示在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:36:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10215v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10215v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Personalized dialogue generation aims to leverage persona profiles and dialogue history to generate persona-relevant and consistent responses. Mainstream models typically rely on token-level language model training with persona dialogue data, such as Next Token Prediction, to implicitly achieve personalization, making these methods tend to neglect the given personas and generate generic responses. To address this issue, we propose a novel Persona-Aware Alignment Framework (PAL), which directly treats persona alignment as the training objective of dialogue generation. Specifically, PAL employs a two-stage training method including Persona-aware Learning and Persona Alignment, equipped with an easy-to-use inference strategy Select then Generate, to improve persona sensitivity and generate more persona-relevant responses at the semantics level. Through extensive experiments, we demonstrate that our framework outperforms many state-of-the-art personalized dialogue methods and large language models.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10507v1" target="_blank" rel="noopener noreferrer">
                基于量规的基准测试与强化学习用于提升大语言模型指令遵循能力
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM指令遵循的基准测试和强化学习方法，属于纯粹的LLM评估和训练技术范畴。虽然强化学习可能间接应用于推荐系统或搜索的排序优化，但论文标题明确聚焦于指令遵循这一NLP核心任务，缺乏与RecSys/Search/Ads领域的直接关联或明确的应用潜力说明。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:14:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10507v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10507v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, and system-prompted instructions-remains a significant challenge. Rigorous evaluation and effective training for such capabilities are hindered by the lack of high-quality, human-annotated benchmarks and reliable, interpretable reward signals. In this work, we introduce AdvancedIF (we will release this benchmark soon), a comprehensive benchmark featuring over 1,600 prompts and expert-curated rubrics that assess LLMs ability to follow complex, multi-turn, and system-level instructions. We further propose RIFL (Rubric-based Instruction-Following Learning), a novel post-training pipeline that leverages rubric generation, a finetuned rubric verifier, and reward shaping to enable effective reinforcement learning for instruction following. Extensive experiments demonstrate that RIFL substantially improves the instruction-following abilities of LLMs, achieving a 6.7% absolute gain on AdvancedIF and strong results on public benchmarks. Our ablation studies confirm the effectiveness of each component in RIFL. This work establishes rubrics as a powerful tool for both training and evaluating advanced IF in LLMs, paving the way for more capable and reliable AI systems.
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            <a href="https://www.alphaxiv.org/abs/2511.10621v1" target="_blank" rel="noopener noreferrer">
                SSR：用于大语言模型推理的苏格拉底式自优化
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            SSR: Socratic Self-Refine for Large Language Model Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haizhou Shi, Ye Liu, Bo Pang, Zeyu Leo Liu, Hao Wang, Silvio Savarese, Caiming X...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM推理能力的自我优化方法，属于核心LLM技术进展。虽然推理优化可能间接提升推荐和搜索系统中的复杂问题解决能力，但论文标题未明确表明与推荐系统、搜索或广告的直接应用关联，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:47:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10621v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10621v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, yet existing test-time frameworks often rely on coarse self-verification and self-correction, limiting their effectiveness on complex tasks. In this paper, we propose Socratic Self-Refine (SSR), a novel framework for fine-grained evaluation and precise refinement of LLM reasoning. Our proposed SSR decomposes model responses into verifiable (sub-question, sub-answer) pairs, enabling step-level confidence estimation through controlled re-solving and self-consistency checks. By pinpointing unreliable steps and iteratively refining them, SSR produces more accurate and interpretable reasoning chains. Empirical results across five reasoning benchmarks and three LLMs show that SSR consistently outperforms state-of-the-art iterative self-refinement baselines. Beyond performance gains, SSR provides a principled black-box approach for evaluating and understanding the internal reasoning processes of LLMs. Code is available at https://github.com/SalesforceAIResearch/socratic-self-refine-reasoning.
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            <a href="https://www.alphaxiv.org/abs/2511.10081v1" target="_blank" rel="noopener noreferrer">
                GridPrune：从“看哪里”到“选什么”——多模态大语言模型中的视觉令牌剪枝
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            GridPrune: From "Where to Look" to "What to Select" in Visual Token Pruning for MLLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxiang Duan, Ao Li, Yingqin Li, Luyu Li, Pengwei Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型中的视觉令牌剪枝技术，属于视觉效率优化范畴。虽然剪枝技术可能间接提升模型效率，但论文焦点在视觉模态而非推荐/搜索/广告的核心排序或检索任务。对于推荐/搜索/广告的潜在应用有限，主要适用于需要视觉处理的特定场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:35:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10081v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10081v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal large language models (MLLMs) have shown remarkable capabilities in a wide range of vision-language tasks. However, the large number of visual tokens introduces significant computational overhead. To address this issue, visual token pruning has emerged as a key technique for enhancing the efficiency of MLLMs. In cognitive science, humans tend to first determine which regions of a scene to attend to ("where to look") before deciding which specific elements within those regions to process in detail ("what to select"). This two-stage strategy enables the visual system to efficiently allocate attention at a coarse spatial level before performing fine-grained selection. However, existing pruning methods primarily focus on directly optimizing "what to select", typically using attention scores or similarity metrics. They rarely consider "where to look", which has been shown to lead to inefficient spatial allocation, positional bias, and the retention of irrelevant or redundant tokens. In this paper, we propose GridPrune, a method that replaces the global Top-K mechanism with a "guide-globally, select-locally" zonal selection system. GridPrune splits the pruning process into two steps: first, it uses text-conditional guidance to dynamically allocate a token budget across spatial zones; and then, it performs local selection within each budgeted zone. Experimental results demonstrate that GridPrune achieves superior performance across various MLLM architectures. On LLaVA-NeXT-7B, GridPrune retains 96.98% of the full performance while using 11.1% of the tokens, outperforming the best-performing baseline by 2.34% at the same pruning rate.
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            <a href="https://www.alphaxiv.org/abs/2511.10154v1" target="_blank" rel="noopener noreferrer">
                GEA：面向文本到图像人物检索的生成增强对齐方法
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            GEA: Generation-Enhanced Alignment for Text-to-Image Person Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Zou, Runqing Zhang, Xue Zhou, Jianxiao Zou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及文本到图像检索，与搜索领域有一定相关性，但主要聚焦于人物检索这一特定视觉任务。虽然提到了生成增强方法，但其核心是视觉-文本跨模态检索而非推荐系统或广告应用。该技术可能适用于电商搜索中的商品图像检索，但应用范围相对狭窄。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 10:06:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10154v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10154v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Text-to-Image Person Retrieval (TIPR) aims to retrieve person images based on natural language descriptions. Although many TIPR methods have achieved promising results, sometimes textual queries cannot accurately and comprehensively reflect the content of the image, leading to poor cross-modal alignment and overfitting to limited datasets. Moreover, the inherent modality gap between text and image further amplifies these issues, making accurate cross-modal retrieval even more challenging. To address these limitations, we propose the Generation-Enhanced Alignment (GEA) from a generative perspective. GEA contains two parallel modules: (1) Text-Guided Token Enhancement (TGTE), which introduces diffusion-generated images as intermediate semantic representations to bridge the gap between text and visual patterns. These generated images enrich the semantic representation of text and facilitate cross-modal alignment. (2) Generative Intermediate Fusion (GIF), which combines cross-attention between generated images, original images, and text features to generate a unified representation optimized by triplet alignment loss. We conduct extensive experiments on three public TIPR datasets, CUHK-PEDES, RSTPReid, and ICFG-PEDES, to evaluate the performance of GEA. The results justify the effectiveness of our method. More implementation details and extended results are available at https://github.com/sugelamyd123/Sup-for-GEA.
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            <a href="https://www.alphaxiv.org/abs/2511.09883v1" target="_blank" rel="noopener noreferrer">
                HCC-3D：面向视觉语言模型的层次化补偿压缩，实现98%的3D令牌减少
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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            HCC-3D: Hierarchical Compensatory Compression for 98% 3D Token Reduction in Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liheng Zhang, Jin Wang, Hui Li, Bingfeng Zhang, Weifeng Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D视觉数据的压缩技术，虽然涉及视觉语言模型，但其核心是3D令牌减少，这与RecSys/Search/Ads中处理的主要数据类型（文本、用户行为序列、上下文特征）相关性较低。作为使能技术，高效的3D数据处理可能应用于处理3D产品模型或空间数据的广告/搜索场景，但应用范围相对狭窄且非主流。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:28:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09883v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09883v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D understanding has drawn significant attention recently, leveraging Vision-Language Models (VLMs) to enable multi-modal reasoning between point cloud and text data. Current 3D-VLMs directly embed the 3D point clouds into 3D tokens, following large 2D-VLMs with powerful reasoning capabilities. However, this framework has a great computational cost limiting its application, where we identify that the bottleneck lies in processing all 3D tokens in the Large Language Model (LLM) part. This raises the question: how can we reduce the computational overhead introduced by 3D tokens while preserving the integrity of their essential information? To address this question, we introduce Hierarchical Compensatory Compression (HCC-3D) to efficiently compress 3D tokens while maintaining critical detail retention. Specifically, we first propose a global structure compression (GSC), in which we design global queries to compress all 3D tokens into a few key tokens while keeping overall structural information. Then, to compensate for the information loss in GSC, we further propose an adaptive detail mining (ADM) module that selectively recompresses salient but under-attended features through complementary scoring. Extensive experiments demonstrate that HCC-3D not only achieves extreme compression ratios (approximately 98%) compared to previous 3D-VLMs, but also achieves new state-of-the-art performance, showing the great improvements on both efficiency and performance.
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            <a href="https://www.alphaxiv.org/abs/2511.10627v1" target="_blank" rel="noopener noreferrer">
                使用场景程序查询标记时间序列数据
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Querying Labeled Time Series Data with Scenario Programs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Edward Kim, Devan Shanker, Varun Bharadwaj, Hongbeen Park, Jinkyu Kim, Hazem Tor...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注时间序列数据的查询方法，在推荐系统或搜索中可能用于处理用户行为序列数据。然而，标题未明确涉及推荐系统、搜索或广告的核心问题，也未提及LLM、Transformer或异构数据建模等关键技术，潜在应用较为间接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:52:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10627v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10627v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.FL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Simulation-based testing has become a crucial complement to road testing for ensuring the safety of cyber physical systems (CPS). As a result, significant research efforts have been directed toward identifying failure scenarios within simulation environments. However, a critical question remains. Are the AV failure scenarios discovered in simulation reproducible on actual systems in the real world? The sim-to-real gap caused by differences between simulated and real sensor data means that failure scenarios identified in simulation might either be artifacts of synthetic sensor data or actual issues that also occur with real sensor data. To address this, an effective approach to validating simulated failure scenarios is to locate occurrences of these scenarios within real-world datasets and verify whether the failure persists on the datasets. To this end, we introduce a formal definition of how labeled time series sensor data can match an abstract scenario, represented as a scenario program using the Scenic probabilistic programming language. We present a querying algorithm that, given a scenario program and a labeled dataset, identifies the subset of data that matches the specified scenario. Our experiment shows that our algorithm is more accurate and orders of magnitude faster in querying scenarios than the state-of-the-art commercial vision large language models, and can scale with the duration of queried time series data.
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            <a href="https://www.alphaxiv.org/abs/2511.09973v1" target="_blank" rel="noopener noreferrer">
                面向视觉语言模型鲁棒微调的差分向量均衡
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Difference Vector Equalization for Robust Fine-tuning of Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda, Taiga Yamane, Naoki Makishi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及视觉语言模型（VLM）的微调技术，但其核心焦点是提升模型的鲁棒性，而非处理异构数据或推荐/搜索/广告领域的特定应用。差分向量均衡技术可能对模型泛化有帮助，但在推荐系统中处理异构用户序列和上下文特征的直接应用潜力有限，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 05:11:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09973v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09973v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Contrastive pre-trained vision-language models, such as CLIP, demonstrate strong generalization abilities in zero-shot classification by leveraging embeddings extracted from image and text encoders. This paper aims to robustly fine-tune these vision-language models on in-distribution (ID) data without compromising their generalization abilities in out-of-distribution (OOD) and zero-shot settings. Current robust fine-tuning methods tackle this challenge by reusing contrastive learning, which was used in pre-training, for fine-tuning. However, we found that these methods distort the geometric structure of the embeddings, which plays a crucial role in the generalization of vision-language models, resulting in limited OOD and zero-shot performance. To address this, we propose Difference Vector Equalization (DiVE), which preserves the geometric structure during fine-tuning. The idea behind DiVE is to constrain difference vectors, each of which is obtained by subtracting the embeddings extracted from the pre-trained and fine-tuning models for the same data sample. By constraining the difference vectors to be equal across various data samples, we effectively preserve the geometric structure. Therefore, we introduce two losses: average vector loss (AVL) and pairwise vector loss (PVL). AVL preserves the geometric structure globally by constraining difference vectors to be equal to their weighted average. PVL preserves the geometric structure locally by ensuring a consistent multimodal alignment. Our experiments demonstrate that DiVE effectively preserves the geometric structure, achieving strong results across ID, OOD, and zero-shot metrics.
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            <a href="https://www.alphaxiv.org/abs/2511.10648v1" target="_blank" rel="noopener noreferrer">
                通过自一致性采样增强基于结果奖励的多模态大语言模型强化学习训练
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahao Wang, Weiye Xu, Aijun Yang, Wengang Zhou, Lewei Lu, Houqiang Li, Xiaohua ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型（MLLM）的强化学习训练方法，属于纯粹的LLM训练技术范畴。虽然提到了强化学习和奖励机制，但缺乏明确的推荐系统、搜索或广告应用场景的直接关联，且核心焦点是MLLM训练而非这些领域的特定应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10648v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10648v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Outcome-reward reinforcement learning (RL) is a common and increasingly significant way to refine the step-by-step reasoning of multimodal large language models (MLLMs). In the multiple-choice setting - a dominant format for multimodal reasoning benchmarks - the paradigm faces a significant yet often overlooked obstacle: unfaithful trajectories that guess the correct option after a faulty chain of thought receive the same reward as genuine reasoning, which is a flaw that cannot be ignored. We propose Self-Consistency Sampling (SCS) to correct this issue. For each question, SCS (i) introduces small visual perturbations and (ii) performs repeated truncation and resampling of an initial trajectory; agreement among the resulting trajectories yields a differentiable consistency score that down-weights unreliable traces during policy updates. Based on Qwen2.5-VL-7B-Instruct, plugging SCS into RLOO, GRPO, and REINFORCE++ series improves accuracy by up to 7.7 percentage points on six multimodal benchmarks with negligible extra computation. SCS also yields notable gains on both Qwen2.5-VL-3B-Instruct and InternVL3-8B, offering a simple, general remedy for outcome-reward RL in MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.10277v1" target="_blank" rel="noopener noreferrer">
                具有模块化记忆的固定角色小型语言模型：在消费级硬件上实现可扩展的NPC对话
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            <i class="fa fa-star mr-1"></i>2/10
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            Fixed-Persona SLMs with Modular Memory: Scalable NPC Dialogue on Consumer Hardware
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Martin Braas, Lukas Esterle
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注游戏NPC对话系统，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然提到了小型语言模型和模块化架构，但这些技术改进主要针对游戏对话场景，缺乏明确的RecSys/Search/Ads应用潜力。论文焦点与我的核心关注领域重叠度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:03:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10277v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10277v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, yet their applicability to dialogue systems in computer games remains limited. This limitation arises from their substantial hardware requirements, latency constraints, and the necessity to maintain clearly defined knowledge boundaries within a game setting. In this paper, we propose a modular NPC dialogue system that leverages Small Language Models (SLMs), fine-tuned to encode specific NPC personas and integrated with runtime-swappable memory modules. These memory modules preserve character-specific conversational context and world knowledge, enabling expressive interactions and long-term memory without retraining or model reloading during gameplay. We comprehensively evaluate our system using three open-source SLMs: DistilGPT-2, TinyLlama-1.1B-Chat, and Mistral-7B-Instruct, trained on synthetic persona-aligned data and benchmarked on consumer-grade hardware. While our approach is motivated by applications in gaming, its modular design and persona-driven memory architecture hold significant potential for broader adoption in domains requiring expressive, scalable, and memory-rich conversational agents, such as virtual assistants, customer support bots, or interactive educational systems.
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            <a href="https://www.alphaxiv.org/abs/2511.10523v1" target="_blank" rel="noopener noreferrer">
                Convomem基准测试：为什么您的前150次对话不需要检索增强生成
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            <i class="fa fa-star mr-1"></i>2/10
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            Convomem Benchmark: Why Your First 150 Conversations Don't Need RAG
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Egor Pakhomov, Erik Nijkamp, Caiming Xiong
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注对话系统中的检索增强生成(RAG)技术，这属于纯粹的LLM应用领域而非推荐系统、搜索或广告的核心技术。虽然RAG在信息检索中有应用，但论文标题暗示的是对话场景下的技术优化，没有明确指向RecSys/Search/Ads领域的直接应用或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:25:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10523v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10523v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce a comprehensive benchmark for conversational memory evaluation containing 75,336 question-answer pairs across diverse categories including user facts, assistant recall, abstention, preferences, temporal changes, and implicit connections. While existing benchmarks have advanced the field, our work addresses fundamental challenges in statistical power, data generation consistency, and evaluation flexibility that limit current memory evaluation frameworks. We examine the relationship between conversational memory and retrieval-augmented generation (RAG). While these systems share fundamental architectural patterns--temporal reasoning, implicit extraction, knowledge updates, and graph representations--memory systems have a unique characteristic: they start from zero and grow progressively with each conversation. This characteristic enables naive approaches that would be impractical for traditional RAG. Consistent with recent findings on long context effectiveness, we observe that simple full-context approaches achieve 70-82% accuracy even on our most challenging multi-message evidence cases, while sophisticated RAG-based memory systems like Mem0 achieve only 30-45% when operating on conversation histories under 150 interactions. Our analysis reveals practical transition points: long context excels for the first 30 conversations, remains viable with manageable trade-offs up to 150 conversations, and typically requires hybrid or RAG approaches beyond that point as costs and latencies become prohibitive. These patterns indicate that the small-corpus advantage of conversational memory--where exhaustive search and complete reranking are feasible--deserves dedicated research attention rather than simply applying general RAG solutions to conversation histories.
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            <a href="https://www.alphaxiv.org/abs/2511.10201v1" target="_blank" rel="noopener noreferrer">
                EffiReason-Bench：用于评估和推进大型语言模型高效推理的统一基准
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            EffiReason-Bench: A Unified Benchmark for Evaluating and Advancing Efficient Reasoning in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junquan Huang, Haotian Wu, Yubo Gao, Yibo Yan, Junyan Zhang, Yonghua Hei, Song D...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM推理效率的基准测试和评估，属于纯粹的LLM评估基准范畴。虽然推理效率是重要的技术问题，但论文标题明确表明其焦点是基准测试和评估，而非直接应用于推荐系统、搜索或广告的实际技术进展。根据筛选标准，这属于'纯粹NLP中心主题'中的'评估基准'类别，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:14:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10201v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10201v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) with Chain-of-Thought (CoT) prompting achieve strong reasoning but often produce unnecessarily long explanations, increasing cost and sometimes reducing accuracy. Fair comparison of efficiency-oriented approaches is hindered by fragmented evaluation practices. We introduce EffiReason-Bench, a unified benchmark for rigorous cross-paradigm evaluation of efficient reasoning methods across three categories: Reasoning Blueprints, Dynamic Execution, and Post-hoc Refinement. To enable step-by-step evaluation, we construct verified CoT annotations for CommonsenseQA and LogiQA via a pipeline that enforces standardized reasoning structures, comprehensive option-wise analysis, and human verification. We evaluate 7 methods across 6 open-source LLMs (1B-70B) on 4 datasets spanning mathematics, commonsense, and logic, and propose the E3-Score, a principled metric inspired by economic trade-off modeling that provides smooth, stable evaluation without discontinuities or heavy reliance on heuristics. Experiments show that no single method universally dominates; optimal strategies depend on backbone scale, task complexity, and architecture.
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            <a href="https://www.alphaxiv.org/abs/2511.09918v1" target="_blank" rel="noopener noreferrer">
                MINDS：一个用于社会规范分类与遵循检测的跨文化对话语料库
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            MINDS: A Cross-cultural Dialogue Corpus for Social Norm Classification and Adherence Detection
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pritish Sahu, Anirudh Som, Dimitra Vergyri, Ajay Divakaran
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注社会规范分类和对话语料库构建，属于社会计算和对话系统领域。虽然对话数据在推荐和搜索中有潜在应用，但该论文的核心焦点是跨文化社会规范分析，与当前关注的LLM技术、推荐系统架构、Transformer改进或异构数据统一建模等核心技术方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:33:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09918v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09918v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Social norms are implicit, culturally grounded expectations that guide interpersonal communication. Unlike factual commonsense, norm reasoning is subjective, context-dependent, and varies across cultures, posing challenges for computational models. Prior works provide valuable normative annotations but mostly target isolated utterances or synthetic dialogues, limiting their ability to capture the fluid, multi-turn nature of real-world conversations. In this work, we present Norm-RAG, a retrieval-augmented, agentic framework for nuanced social norm inference in multi-turn dialogues. Norm-RAG models utterance-level attributes including communicative intent, speaker roles, interpersonal framing, and linguistic cues and grounds them in structured normative documentation retrieved via a novel Semantic Chunking approach. This enables interpretable and context-aware reasoning about norm adherence and violation across multilingual dialogues. We further introduce MINDS (Multilingual Interactions with Norm-Driven Speech), a bilingual dataset comprising 31 multi-turn Mandarin-English and Spanish-English conversations. Each turn is annotated for norm category and adherence status using multi-annotator consensus, reflecting cross-cultural and realistic norm expression. Our experiments show that Norm-RAG improves norm detection and generalization, demonstrates improved performance for culturally adaptive and socially intelligent dialogue systems.
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            <a href="https://www.alphaxiv.org/abs/2511.09880v1" target="_blank" rel="noopener noreferrer">
                EnchTable：微调后大语言模型中的统一安全对齐迁移
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            EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jialin Wu, Kecen Li, Zhicong Huang, Xinfeng Li, Xiaofeng Wang, Cheng Hong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM安全对齐技术，属于模型安全性和伦理对齐领域，这在您的无关主题列表中明确排除。虽然安全对齐是LLM技术的一部分，但论文没有展示在推荐系统、搜索或广告中的潜在应用，也没有涉及核心架构创新或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:26:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09880v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09880v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and mathematical problem solving. However, this fine-tuning process often introduces a critical vulnerability: the systematic degradation of safety alignment, undermining ethical guidelines and increasing the risk of harmful outputs. Addressing this challenge, we introduce EnchTable, a novel framework designed to transfer and maintain safety alignment in downstream LLMs without requiring extensive retraining. EnchTable leverages a Neural Tangent Kernel (NTK)-based safety vector distillation method to decouple safety constraints from task-specific reasoning, ensuring compatibility across diverse model architectures and sizes. Additionally, our interference-aware merging technique effectively balances safety and utility, minimizing performance compromises across various task domains. We implemented a fully functional prototype of EnchTable on three different task domains and three distinct LLM architectures, and evaluated its performance through extensive experiments on eleven diverse datasets, assessing both utility and model safety. Our evaluations include LLMs from different vendors, demonstrating EnchTable's generalization capability. Furthermore, EnchTable exhibits robust resistance to static and dynamic jailbreaking attacks, outperforming vendor-released safety models in mitigating adversarial prompts. Comparative analyses with six parameter modification methods and two inference-time alignment baselines reveal that EnchTable achieves a significantly lower unsafe rate, higher utility score, and universal applicability across different task domains. Additionally, we validate EnchTable can be seamlessly integrated into various deployment pipelines without significant overhead.
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            <a href="https://www.alphaxiv.org/abs/2511.10441v1" target="_blank" rel="noopener noreferrer">
                类比结构、最小上下文线索与对比干扰项：面向样本高效语言规则归纳的输入设计
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        <div class="mb-2 text-base text-gray-700">
            Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chunyang Jiang, Paola Merlo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语言规则归纳的输入设计方法，属于纯粹的语言学/NLP研究范畴。虽然提到了样本效率和对比学习等技术概念，但主要应用于语言规则学习这一特定任务，没有明确展示在推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:04:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10441v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10441v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models achieve strong performance through training on vast datasets. Can analogical paradigm organization enable lightweight models to match this performance with minimal data? We develop a computational approach implementing three cognitive-inspired principles: analogical structure, contrastive learning, and minimal contextual cues. We test this approach with structured completion tasks where models identify correct sentence completions from analogical patterns with contrastive alternatives. Training lightweight models (BERT+CNN, $0.5M$ parameters) on only one hundred structured examples of English causative/inchoative alternations achieves $F1=0.95$, outperforming zero-shot \texttt{GPT-o3} ($F1=0.87$). Ablation studies confirm that analogical organization and contrastive structure improve performance, consistently surpassing randomly shuffled baselines across architectures. Cross-phenomenon validation using unspecified object alternations replicates these efficiency gains, confirming approach robustness. Our results show that analogical paradigm organization enables competitive linguistic rule learning with orders of magnitude less data than conventional approaches require.
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                位置：关于评估基础大语言模型推理能力的方法论陷阱
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            Position: On the Methodological Pitfalls of Evaluating Base LLMs for Reasoning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jason Chan, Zhixue Zhao, Robert Gaizauskas
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注基础LLM的评估方法论问题，特别是推理能力的评估陷阱。虽然涉及LLM技术，但焦点是评估基准和方法论问题，这属于纯粹的NLP评估范畴，与RecSys/Search/Ads的核心技术进展或应用没有直接关联。论文没有展示任何在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:55:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10381v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10381v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Existing work investigates the reasoning capabilities of large language models (LLMs) to uncover their limitations, human-like biases and underlying processes. Such studies include evaluations of base LLMs (pre-trained on unlabeled corpora only) for this purpose. Our position paper argues that evaluating base LLMs' reasoning capabilities raises inherent methodological concerns that are overlooked in such existing studies. We highlight the fundamental mismatch between base LLMs' pretraining objective and normative qualities, such as correctness, by which reasoning is assessed. In particular, we show how base LLMs generate logically valid or invalid conclusions as coincidental byproducts of conforming to purely linguistic patterns of statistical plausibility. This fundamental mismatch challenges the assumptions that (a) base LLMs' outputs can be assessed as their bona fide attempts at correct answers or conclusions; and (b) conclusions about base LLMs' reasoning can generalize to post-trained LLMs optimized for successful instruction-following. We call for a critical re-examination of existing work that relies implicitly on these assumptions, and for future work to account for these methodological pitfalls.
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            <a href="https://www.alphaxiv.org/abs/2511.09926v1" target="_blank" rel="noopener noreferrer">
                预训练视觉Transformer的类增量学习中分布漂移的补偿
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            Compensating Distribution Drifts in Class-incremental Learning of Pre-trained Vision Transformers
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuan Rao, Simian Xu, Zheng Li, Bo Zhao, Derong Liu, Mingming Ha, Cesare Alippi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的类增量学习问题，虽然涉及Transformer架构，但焦点是视觉任务的分布漂移补偿。这与推荐系统、搜索或广告的核心技术需求关联较弱，因为分布漂移补偿在推荐/搜索中通常通过在线学习、特征工程或专门的漂移检测方法处理，而非视觉任务的类增量学习框架。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:40:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09926v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09926v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances have shown that sequential fine-tuning (SeqFT) of pre-trained vision transformers (ViTs), followed by classifier refinement using approximate distributions of class features, can be an effective strategy for class-incremental learning (CIL). However, this approach is susceptible to distribution drift, caused by the sequential optimization of shared backbone parameters. This results in a mismatch between the distributions of the previously learned classes and that of the updater model, ultimately degrading the effectiveness of classifier performance over time. To address this issue, we introduce a latent space transition operator and propose Sequential Learning with Drift Compensation (SLDC). SLDC aims to align feature distributions across tasks to mitigate the impact of drift. First, we present a linear variant of SLDC, which learns a linear operator by solving a regularized least-squares problem that maps features before and after fine-tuning. Next, we extend this with a weakly nonlinear SLDC variant, which assumes that the ideal transition operator lies between purely linear and fully nonlinear transformations. This is implemented using learnable, weakly nonlinear mappings that balance flexibility and generalization. To further reduce representation drift, we apply knowledge distillation (KD) in both algorithmic variants. Extensive experiments on standard CIL benchmarks demonstrate that SLDC significantly improves the performance of SeqFT. Notably, by combining KD to address representation drift with SLDC to compensate distribution drift, SeqFT achieves performance comparable to joint training across all evaluated datasets. Code: https://github.com/raoxuan98-hash/sldc.git.
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            <a href="https://www.alphaxiv.org/abs/2511.09854v1" target="_blank" rel="noopener noreferrer">
                TermGPT：面向法律和金融领域术语适应的多层级对比微调
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            TermGPT: Multi-Level Contrastive Fine-Tuning for Terminology Adaptation in Legal and Financial Domain
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yidan Sun, Mengying Zhu, Feiyue Chen, Yangyang Wu, Xiaolei Dan, Mengyuan Yang, X...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定领域（法律和金融）的术语适应，这属于领域特定的NLP应用，与推荐系统、搜索或广告的核心技术焦点无关。虽然涉及对比学习和微调技术，但这些方法在当前焦点中缺乏明确的RecSys/Search/Ads应用潜力，且领域限制使其相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 01:25:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09854v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09854v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) have demonstrated impressive performance in text generation tasks; however, their embedding spaces often suffer from the isotropy problem, resulting in poor discrimination of domain-specific terminology, particularly in legal and financial contexts. This weakness in terminology-level representation can severely hinder downstream tasks such as legal judgment prediction or financial risk analysis, where subtle semantic distinctions are critical. To address this problem, we propose TermGPT, a multi-level contrastive fine-tuning framework designed for terminology adaptation. We first construct a sentence graph to capture semantic and structural relations, and generate semantically consistent yet discriminative positive and negative samples based on contextual and topological cues. We then devise a multi-level contrastive learning approach at both the sentence and token levels, enhancing global contextual understanding and fine-grained terminology discrimination. To support robust evaluation, we construct the first financial terminology dataset derived from official regulatory documents. Experiments show that TermGPT outperforms existing baselines in term discrimination tasks within the finance and legal domains.
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            <a href="https://www.alphaxiv.org/abs/2511.09831v1" target="_blank" rel="noopener noreferrer">
                使用多链思维推理和微调RAG增强大语言模型回答课程论坛中学生问题
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            Answering Students' Questions on Course Forums Using Multiple Chain-of-Thought Reasoning and Finetuning RAG-Enabled LLM
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Neo Wang, Sonit Singh
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注教育领域的问答应用，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及RAG和思维链推理等LLM技术，但应用场景与RecSys/Search/Ads领域没有直接关联，且没有展示这些技术如何应用于推荐或搜索系统的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 00:26:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09831v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09831v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                    The course forums are increasingly significant and play vital role in facilitating student discussions and answering their questions related to the course. It provides a platform for students to post their questions related to the content and admin issues related to the course. However, there are several challenges due to the increase in the number of students enrolled in the course. The primary challenge is that students' queries cannot be responded immediately and the instructors have to face lots of repetitive questions. To mitigate these issues, we propose a question answering system based on large language model with retrieval augmented generation (RAG) method. This work focuses on designing a question answering system with open source Large Language Model (LLM) and fine-tuning it on the relevant course dataset. To further improve the performance, we use a local knowledge base and applied RAG method to retrieve relevant documents relevant to students' queries, where the local knowledge base contains all the course content. To mitigate the hallucination of LLMs, We also integrate it with multi chain-of-thought reasoning to overcome the challenge of hallucination in LLMs. In this work, we experiment fine-tuned LLM with RAG method on the HotpotQA dataset. The experimental results demonstrate that the fine-tuned LLM with RAG method has a strong performance on question answering task.
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            <a href="https://www.alphaxiv.org/abs/2511.10354v1" target="_blank" rel="noopener noreferrer">
                从文化遗产文本生成知识图谱：结合大语言模型与本体工程用于学术辩论
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            Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Andrea Schimmenti, Valentina Pasqual, Fabio Vitali, Marieke van Erp
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文化遗产领域的知识图谱生成和学术辩论应用，属于特定领域应用而非核心推荐/搜索/广告技术的进展。虽然涉及LLMs技术，但其应用场景（文化遗产、学术辩论）与我的关注领域（推荐系统、搜索、广告）关联度极低，且没有明确的跨领域应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:29:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10354v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10354v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse into structured Knowledge Graphs (KGs). This paper introduces ATR4CH (Adaptive Text-to-RDF for Cultural Heritage), a systematic five-step methodology for Large Language Model-based Knowledge Extraction from Cultural Heritage documents. We validate the methodology through a case study on authenticity assessment debates. Methodology - ATR4CH combines annotation models, ontological frameworks, and LLM-based extraction through iterative development: foundational analysis, annotation schema development, pipeline architecture, integration refinement, and comprehensive evaluation. We demonstrate the approach using Wikipedia articles about disputed items (documents, artifacts...), implementing a sequential pipeline with three LLMs (Claude Sonnet 3.7, Llama 3.3 70B, GPT-4o-mini). Findings - The methodology successfully extracts complex Cultural Heritage knowledge: 0.96-0.99 F1 for metadata extraction, 0.7-0.8 F1 for entity recognition, 0.65-0.75 F1 for hypothesis extraction, 0.95-0.97 for evidence extraction, and 0.62 G-EVAL for discourse representation. Smaller models performed competitively, enabling cost-effective deployment. Originality - This is the first systematic methodology for coordinating LLM-based extraction with Cultural Heritage ontologies. ATR4CH provides a replicable framework adaptable across CH domains and institutional resources. Research Limitations - The produced KG is limited to Wikipedia articles. While the results are encouraging, human oversight is necessary during post-processing. Practical Implications - ATR4CH enables Cultural Heritage institutions to systematically convert textual knowledge into queryable KGs, supporting automated metadata enrichment and knowledge discovery.
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            <a href="https://www.alphaxiv.org/abs/2511.10289v1" target="_blank" rel="noopener noreferrer">
                Music Flamingo：音频语言模型中的音乐理解规模化
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            Music Flamingo: Scaling Music Understanding in Audio Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sreyan Ghosh, Arushi Goel, Lasha Koroshinadze, Sang-gil Lee, Zhifeng Kong, Joao ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频语言模型中的音乐理解，属于特定领域（音乐）的多模态应用，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然涉及多模态建模概念，但缺乏明确的推荐/搜索/广告应用场景，且音乐领域属于被排除的特定应用领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:21:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10289v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10289v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.CL</span></div>
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                    We introduce Music Flamingo, a novel large audio-language model designed to advance music (including song) understanding in foundational audio models. While audio-language research has progressed rapidly, music remains challenging due to its dynamic, layered, and information-dense nature. Progress has been further limited by the difficulty of scaling open audio understanding models, primarily because of the scarcity of high-quality music data and annotations. As a result, prior models are restricted to producing short, high-level captions, answering only surface-level questions, and showing limited generalization across diverse musical cultures. To address these challenges, we curate MF-Skills, a large-scale dataset labeled through a multi-stage pipeline that yields rich captions and question-answer pairs covering harmony, structure, timbre, lyrics, and cultural context. We fine-tune an enhanced Audio Flamingo 3 backbone on MF-Skills and further strengthen multiple skills relevant to music understanding. To improve the model's reasoning abilities, we introduce a post-training recipe: we first cold-start with MF-Think, a novel chain-of-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards. Music Flamingo achieves state-of-the-art results across 10+ benchmarks for music understanding and reasoning, establishing itself as a generalist and musically intelligent audio-language model. Beyond strong empirical results, Music Flamingo sets a new standard for advanced music understanding by demonstrating how models can move from surface-level recognition toward layered, human-like perception of songs. We believe this work provides both a benchmark and a foundation for the community to build the next generation of models that engage with music as meaningfully as humans do.
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            <a href="https://www.alphaxiv.org/abs/2511.10262v1" target="_blank" rel="noopener noreferrer">
                MTR-DuplexBench：面向全双工语音语言模型多轮对话的全面评估基准
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            MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>He Zhang, Wenqian Cui, Haoning Xu, Xiaohui Li, Lei Zhu, Shaohua Ma, Irwin King
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语音语言模型的评估基准和多轮对话能力，属于语音处理和评估领域。虽然语音交互在搜索和推荐中有潜在应用，但论文重点在于评估基准而非核心推荐/搜索算法或LLM架构创新。与当前关注的LLM技术趋势、Transformer架构改进或直接应用相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:50:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10262v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10262v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">eess.AS</span></div>
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                    Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experience compared to traditional half-duplex models. However, existing benchmarks primarily focus on evaluating single-round interactions and conversational features, neglecting the complexities of multi-round communication and critical capabilities such as instruction following and safety. Evaluating FD-SLMs in multi-round settings poses significant challenges, including blurred turn boundaries in communication and context inconsistency during model inference. To address these gaps, we introduce MTR-DuplexBench, a novel benchmark that segments continuous full-duplex dialogues into discrete turns, enabling comprehensive, turn-by-turn evaluation of FD-SLMs across dialogue quality, conversational dynamics, instruction following, and safety. Experimental results reveal that current FD-SLMs face difficulties in maintaining consistent performance across multiple rounds and evaluation dimensions, highlighting the necessity and effectiveness of our proposed benchmark. The benchmark and code will be available in the future.
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                了解你的极限：面向压缩与泛化的熵估计建模
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Know Your Limits: Entropy Estimation Modeling for Compression and Generalization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Benjamin L. Badger, Matthew Neligeorge
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注熵估计建模，主要涉及数据压缩和模型泛化，属于通用的机器学习技术。虽然压缩技术可能间接应用于推荐或搜索系统的模型部署效率，但论文标题没有明确指向推荐系统、搜索或广告领域的特定应用，也没有涉及LLM或Transformer架构的直接进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:46:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10618v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10618v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IT</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Language prediction is constrained by informational entropy intrinsic to language, such that there exists a limit to how accurate any language model can become and equivalently a lower bound to language compression. The most efficient language compression algorithms today are causal (next token prediction) large language models, but the use of these models to form accurate estimates of language entropy is currently computationally infeasible. We introduce encoder-augmented causal decoder model architectures that exhibit superior training efficiency characteristics and achieve higher compression than causal transformers even when trained on modest hardware. We demonstrate how entropy estimates can be obtained on a per-token basis, and show that the generalization of models trained to approach the entropy of their training data necessarily exceeds the generalization of models trained to minimize loss beyond this value. We show empirically that causal models trained to approach but not exceed estimated per-token entropies exhibit greater generalization than models trained without taking entropy into account.
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            <a href="https://www.alphaxiv.org/abs/2511.09971v1" target="_blank" rel="noopener noreferrer">
                NumPert：通过数值扰动探测语言模型进行真实性预测
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            NumPert: Numerical Perturbations to Probe Language Models for Veracity Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peter Røysland Aarnes, Vinay Setty
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语言模型的真实性预测和数值扰动探测，这属于LLM评估和可信度验证范畴，与我的核心关注点（推荐系统、搜索、广告中的核心进展或LLM技术应用）相关性较弱。虽然涉及语言模型技术，但其应用方向更偏向模型可信度和真实性验证，而非在推荐/搜索/广告领域的直接应用或架构创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 05:09:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09971v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09971v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models show strong performance on knowledge intensive tasks such as fact-checking and question answering, yet they often struggle with numerical reasoning. We present a systematic evaluation of state-of-the-art models for veracity prediction on numerical claims and evidence pairs using controlled perturbations, including label-flipping probes, to test robustness. Our results indicate that even leading proprietary systems experience accuracy drops of up to 62\% under certain perturbations. No model proves to be robust across all conditions. We further find that increasing context length generally reduces accuracy, but when extended context is enriched with perturbed demonstrations, most models substantially recover. These findings highlight critical limitations in numerical fact-checking and suggest that robustness remains an open challenge for current language models.
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            <a href="https://www.alphaxiv.org/abs/2511.10384v1" target="_blank" rel="noopener noreferrer">
                使用大型语言模型模拟社交网络中的虚假信息传播
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Simulating Misinformation Propagation in Social Networks using Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Raj Gaurav Maurya, Vaibhav Shukla, Raj Abhijit Dandekar, Rajat Dandekar, Sreedat...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用LLMs模拟社交网络中的虚假信息传播，这属于社交网络分析和信息传播研究领域。虽然涉及LLMs技术，但核心应用场景（虚假信息传播模拟）与推荐系统、搜索或广告的核心技术进展没有直接关联，且更偏向社交网络分析和信息安全方向，而非排名、检索或个性化推荐等核心业务问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:01:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10384v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10384v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SI</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Misinformation on social media thrives on surprise, emotion, and identity-driven reasoning, often amplified through human cognitive biases. To investigate these mechanisms, we model large language model (LLM) personas as synthetic agents that mimic user-level biases, ideological alignments, and trust heuristics. Within this setup, we introduce an auditor--node framework to simulate and analyze how misinformation evolves as it circulates through networks of such agents. News articles are propagated across networks of persona-conditioned LLM nodes, each rewriting received content. A question--answering-based auditor then measures factual fidelity at every step, offering interpretable, claim-level tracking of misinformation drift. We formalize a misinformation index and a misinformation propagation rate to quantify factual degradation across homogeneous and heterogeneous branches of up to 30 sequential rewrites. Experiments with 21 personas across 10 domains reveal that identity- and ideology-based personas act as misinformation accelerators, especially in politics, marketing, and technology. By contrast, expert-driven personas preserve factual stability. Controlled-random branch simulations further show that once early distortions emerge, heterogeneous persona interactions rapidly escalate misinformation to propaganda-level distortion. Our taxonomy of misinformation severity -- spanning factual errors, lies, and propaganda -- connects observed drift to established theories in misinformation studies. These findings demonstrate the dual role of LLMs as both proxies for human-like biases and as auditors capable of tracing information fidelity. The proposed framework provides an interpretable, empirically grounded approach for studying, simulating, and mitigating misinformation diffusion in digital ecosystems.
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            <a href="https://www.alphaxiv.org/abs/2511.10375v1" target="_blank" rel="noopener noreferrer">
                TruthfulRAG：基于知识图谱解决检索增强生成中事实层面冲突
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            TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuyi Liu, Yuming Shang, Xi Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要解决RAG中的事实冲突问题，这属于LLM评估和可靠性范畴，与RecSys/Search/Ads的核心排名优化关联较弱。虽然检索增强技术本身与搜索相关，但论文聚焦于事实冲突解决而非检索质量提升，在推荐和广告领域的直接应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:49:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10375v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10375v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for enhancing the capabilities of Large Language Models (LLMs) by integrating retrieval-based methods with generative models. As external knowledge repositories continue to expand and the parametric knowledge within models becomes outdated, a critical challenge for RAG systems is resolving conflicts between retrieved external information and LLMs' internal knowledge, which can significantly compromise the accuracy and reliability of generated content. However, existing approaches to conflict resolution typically operate at the token or semantic level, often leading to fragmented and partial understanding of factual discrepancies between LLMs' knowledge and context, particularly in knowledge-intensive tasks. To address this limitation, we propose TruthfulRAG, the first framework that leverages Knowledge Graphs (KGs) to resolve factual-level knowledge conflicts in RAG systems. Specifically, TruthfulRAG constructs KGs by systematically extracting triples from retrieved content, utilizes query-based graph retrieval to identify relevant knowledge, and employs entropy-based filtering mechanisms to precisely locate conflicting elements and mitigate factual inconsistencies, thereby enabling LLMs to generate faithful and accurate responses. Extensive experiments reveal that TruthfulRAG outperforms existing methods, effectively alleviating knowledge conflicts and improving the robustness and trustworthiness of RAG systems.
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            <a href="https://www.alphaxiv.org/abs/2511.10232v1" target="_blank" rel="noopener noreferrer">
                VocalNet-M2：通过集成多码本标记化与多标记预测推进低延迟口语语言建模
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        <div class="mb-2 text-base text-gray-700">
            VocalNet-M2: Advancing Low-Latency Spoken Language Modeling via Integrated Multi-Codebook Tokenization and Multi-Token Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhao Wang, Ziyang Cheng, Heyang Liu, Ronghua Wu, Qunshan Gu, Yanfeng Wang, Yu W...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注口语语言建模的低延迟技术改进，属于语音处理领域。虽然涉及语言建模技术，但其核心应用场景（语音处理）与推荐系统、搜索或广告的关联性较弱，且未明确展示在异构数据处理或Transformer架构效率方面的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:06:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10232v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10232v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.SD</span></div>
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                    Current end-to-end spoken language models (SLMs) have made notable progress, yet they still encounter considerable response latency. This delay primarily arises from the autoregressive generation of speech tokens and the reliance on complex flow-matching models for speech synthesis. To overcome this, we introduce VocalNet-M2, a novel low-latency SLM that integrates a multi-codebook tokenizer and a multi-token prediction (MTP) strategy. Our model directly generates multi-codebook speech tokens, thus eliminating the need for a latency-inducing flow-matching model. Furthermore, our MTP strategy enhances generation efficiency and improves overall performance. Extensive experiments demonstrate that VocalNet-M2 achieves a substantial reduction in first chunk latency (from approximately 725ms to 350ms) while maintaining competitive performance across mainstream SLMs. This work also provides a comprehensive comparison of single-codebook and multi-codebook strategies, offering valuable insights for developing efficient and high-performance SLMs for real-time interactive applications.
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            <a href="https://www.alphaxiv.org/abs/2511.10338v1" target="_blank" rel="noopener noreferrer">
                BhashaKritika：为印度语言大规模构建合成预训练数据
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            BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guduru Manoj, Neel Prabhanjan Rachamalla, Ashish Kulkarni, Gautam Rajeev, Jay Pi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于为印度语言构建合成预训练数据，这属于多语言NLP的数据工程范畴。虽然多语言能力对全球化推荐和搜索系统有潜在价值，但论文核心是特定语言的数据构建技术，而非推荐/搜索/广告领域的核心算法或架构创新。其与当前关注点的直接关联较弱，主要价值在于为多语言应用提供数据基础。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:12:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10338v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10338v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In the context of pretraining of Large Language Models (LLMs), synthetic data has emerged as an alternative for generating high-quality pretraining data at scale. This is particularly beneficial in low-resource language settings where the benefits of recent LLMs have been unevenly distributed across languages. In this work, we present a systematic study on the generation and evaluation of synthetic multilingual pretraining data for Indic languages, where we construct a large-scale synthetic dataset BhashaKritika, comprising 540B tokens using 5 different techniques for 10 languages. We explore the impact of grounding generation in documents, personas, and topics. We analyze how language choice, both in the prompt instructions and document grounding, affects data quality, and we compare translations of English content with native generation in Indic languages. To support scalable and language-sensitive evaluation, we introduce a modular quality evaluation pipeline that integrates script and language detection, metadata consistency checks, n-gram repetition analysis, and perplexity-based filtering using KenLM models. Our framework enables robust quality control across diverse scripts and linguistic contexts. Empirical results through model runs reveal key trade-offs in generation strategies and highlight best practices for constructing effective multilingual corpora.
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            <a href="https://www.alphaxiv.org/abs/2511.10303v1" target="_blank" rel="noopener noreferrer">
                修正评估偏好：通过基于困惑度的强化学习改进大语言模型在数学推理上的评判能力
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            <i class="fa fa-star mr-1"></i>2/10
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            Rectify Evaluation Preference: Improving LLMs' Critique on Math Reasoning via Perplexity-aware Reinforcement Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Changyuan Tian, Zhicong Lu, Shuang Qian, Nayu Liu, Peiguang Li, Li Jin, Leiyi Hu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在数学推理上的评估能力改进，属于纯粹的NLP评估和推理优化范畴。虽然涉及强化学习技术，但缺乏与推荐系统、搜索或广告领域的明确关联。论文的核心目标（数学推理评估）与当前关注的RecSys/Search/Ads应用场景相距甚远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:37:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10303v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10303v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    To improve Multi-step Mathematical Reasoning (MsMR) of Large Language Models (LLMs), it is crucial to obtain scalable supervision from the corpus by automatically critiquing mistakes in the reasoning process of MsMR and rendering a final verdict of the problem-solution. Most existing methods rely on crafting high-quality supervised fine-tuning demonstrations for critiquing capability enhancement and pay little attention to delving into the underlying reason for the poor critiquing performance of LLMs. In this paper, we orthogonally quantify and investigate the potential reason -- imbalanced evaluation preference, and conduct a statistical preference analysis. Motivated by the analysis of the reason, a novel perplexity-aware reinforcement learning algorithm is proposed to rectify the evaluation preference, elevating the critiquing capability. Specifically, to probe into LLMs' critiquing characteristics, a One-to-many Problem-Solution (OPS) benchmark is meticulously constructed to quantify the behavior difference of LLMs when evaluating the problem solutions generated by itself and others. Then, to investigate the behavior difference in depth, we conduct a statistical preference analysis oriented on perplexity and find an intriguing phenomenon -- ``LLMs incline to judge solutions with lower perplexity as correct'', which is dubbed as \textit{imbalanced evaluation preference}. To rectify this preference, we regard perplexity as the baton in the algorithm of Group Relative Policy Optimization, supporting the LLMs to explore trajectories that judge lower perplexity as wrong and higher perplexity as correct. Extensive experimental results on our built OPS and existing available critic benchmarks demonstrate the validity of our method.
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            <a href="https://www.alphaxiv.org/abs/2511.10192v1" target="_blank" rel="noopener noreferrer">
                Text2SQL-Flow：一种面向文本到SQL的鲁棒性SQL感知数据增强框架
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            Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qifeng Cai, Hao Liang, Chang Xu, Tao Xie, Wentao Zhang, Bin Cui
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本到SQL转换的数据增强技术，属于特定NLP任务优化。虽然SQL查询在搜索系统中有所应用，但该工作聚焦于SQL生成的数据增强方法本身，与推荐系统、搜索排名或广告的核心技术关联度较低，且未涉及Transformer架构创新或异构数据统一建模等关键方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:02:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10192v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10192v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.DB</span></div>
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                    The data-centric paradigm has become pivotal in AI, especially for Text-to-SQL, where performance is limited by scarce, simplistic, and low-diversity datasets. To address this, we propose Text2SQL-Flow, a SQL-aware data augmentation framework that generates large-scale, semantically valid, and structurally diverse Text-to-SQL pairs from minimal seed data. It operates across six augmentation dimensions and integrates an end-to-end pipeline featuring SQL execution verification, natural language question generation, chain-of-thought reasoning traces, and data classification. A modular Database Manager ensures cross-database compatibility and scalability. Using this framework, we build SQLFlow, a high-quality dataset of 89,544 annotated examples. We evaluate SQLFlow in two settings: (1) For open-source LLMs, fine-tuning on SQLFlow consistently improves performance across benchmarks under the same data budget. (2) For closed-source LLMs, we introduce a masked alignment retrieval method that treats SQLFlow as both knowledge base and training data for the retriever. This enables structure-aware example matching by modeling fine-grained alignments between questions and SQL queries. Experiments show our retrieval strategy outperforms existing methods, underscoring the value of SQLFlow's high-fidelity data and our novel technique. Our work establishes a scalable, data-centric foundation for advancing Text-to-SQL systems and highlights the critical role of high-quality structured data in modern AI.
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            <a href="https://www.alphaxiv.org/abs/2511.10404v1" target="_blank" rel="noopener noreferrer">
                DELICATE：基于类别与时间证据的历时实体链接
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            DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cristian Santini, Sebastian Barzaghi, Paolo Sernani, Emanuele Frontoni, Mehwish ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注实体链接的时间维度，这在搜索系统中可能有边缘应用（如处理历史查询中的实体消歧），但核心焦点是NLP中的时序实体解析，而非推荐/搜索/广告的核心排序或建模问题。其技术（历时实体链接）与当前关注的LLM、Transformer架构或异构数据统一建模的直接关联性较弱，潜在应用有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:24:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10404v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10404v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    In spite of the remarkable advancements in the field of Natural Language Processing, the task of Entity Linking (EL) remains challenging in the field of humanities due to complex document typologies, lack of domain-specific datasets and models, and long-tail entities, i.e., entities under-represented in Knowledge Bases (KBs). The goal of this paper is to address these issues with two main contributions. The first contribution is DELICATE, a novel neuro-symbolic method for EL on historical Italian which combines a BERT-based encoder with contextual information from Wikidata to select appropriate KB entities using temporal plausibility and entity type consistency. The second contribution is ENEIDE, a multi-domain EL corpus in historical Italian semi-automatically extracted from two annotated editions spanning from the 19th to the 20th century and including literary and political texts. Results show how DELICATE outperforms other EL models in historical Italian even if compared with larger architectures with billions of parameters. Moreover, further analyses reveal how DELICATE confidence scores and features sensitivity provide results which are more explainable and interpretable than purely neural methods.
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            <a href="https://www.alphaxiv.org/abs/2511.10400v1" target="_blank" rel="noopener noreferrer">
                从拜占庭容错视角重新思考多智能体系统的可靠性
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            Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lifan Zheng, Jiawei Chen, Qinghong Yin, Jingyuan Zhang, Xinyi Zeng, Yu Tian
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注多智能体系统的拜占庭容错，属于分布式系统可靠性领域，与推荐系统、搜索或广告的核心技术栈关联度较低。虽然多智能体系统在理论上有潜在应用，但论文标题未表明与LLM、Transformer或推荐/搜索/广告系统的直接联系，且可靠性机制属于基础设施层面而非核心算法创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:20:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10400v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10400v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MA</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Ensuring the reliability of agent architectures and effectively identifying problematic agents when failures occur are crucial challenges in multi-agent systems (MAS). Advances in large language models (LLMs) have established LLM-based agents as a major branch of MAS, enabling major breakthroughs in complex problem solving and world modeling. However, the reliability implications of this shift remain largely unexplored. i.e., whether substituting traditional agents with LLM-based agents can effectively enhance the reliability of MAS. In this work, we investigate and quantify the reliability of LLM-based agents from the perspective of Byzantine fault tolerance. We observe that LLM-based agents demonstrate stronger skepticism when processing erroneous message flows, a characteristic that enables them to outperform traditional agents across different topological structures. Motivated by the results of the pilot experiment, we design CP-WBFT, a confidence probe-based weighted Byzantine Fault Tolerant consensus mechanism to enhance the stability of MAS with different topologies. It capitalizes on the intrinsic reflective and discriminative capabilities of LLMs by employing a probe-based, weighted information flow transmission method to improve the reliability of LLM-based agents. Extensive experiments demonstrate that CP-WBFT achieves superior performance across diverse network topologies under extreme Byzantine conditions (85.7\% fault rate). Notably, our approach surpasses traditional methods by attaining remarkable accuracy on various topologies and maintaining strong reliability in both mathematical reasoning and safety assessment tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.10045v1" target="_blank" rel="noopener noreferrer">
                语言模型是否将声音与意义相关联？关于声音象征性的多模态研究
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            Do Language Models Associate Sound with Meaning? A Multimodal Study of Sound Symbolism
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinhong Jeong, Sunghyun Lee, Jaeyoung Lee, Seonah Han, Youngjae Yu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语言模型中声音与意义的关联性，属于多模态语言理解的基础研究。虽然涉及多模态建模概念，但其核心关注声音象征性这一语言学现象，与推荐系统、搜索或广告的异构数据处理缺乏直接关联，潜在应用场景过于间接和有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:46:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10045v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10045v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Sound symbolism is a linguistic concept that refers to non-arbitrary associations between phonetic forms and their meanings. We suggest that this can be a compelling probe into how Multimodal Large Language Models (MLLMs) interpret auditory information in human languages. We investigate MLLMs' performance on phonetic iconicity across textual (orthographic and IPA) and auditory forms of inputs with up to 25 semantic dimensions (e.g., sharp vs. round), observing models' layer-wise information processing by measuring phoneme-level attention fraction scores. To this end, we present LEX-ICON, an extensive mimetic word dataset consisting of 8,052 words from four natural languages (English, French, Japanese, and Korean) and 2,930 systematically constructed pseudo-words, annotated with semantic features applied across both text and audio modalities. Our key findings demonstrate (1) MLLMs' phonetic intuitions that align with existing linguistic research across multiple semantic dimensions and (2) phonosemantic attention patterns that highlight models' focus on iconic phonemes. These results bridge domains of artificial intelligence and cognitive linguistics, providing the first large-scale, quantitative analyses of phonetic iconicity in terms of MLLMs' interpretability.
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            <a href="https://www.alphaxiv.org/abs/2511.09997v1" target="_blank" rel="noopener noreferrer">
                FinNuE：揭示在金融领域使用BERTScore进行数值语义评估的风险
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            FinNuE: Exposing the Risks of Using BERTScore for Numerical Semantic Evaluation in Finance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu-Shiang Huang, Yun-Yu Lee, Tzu-Hsin Chou, Che Lin, Chuan-Ju Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注金融领域的数值语义评估和BERTScore的风险分析，这属于特定领域应用而非核心推荐系统、搜索或广告的进展。虽然涉及LLM评估，但焦点是金融领域的数值语义问题，与我的关注领域相关性较弱，且没有明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:04:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09997v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09997v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    BERTScore has become a widely adopted metric for evaluating semantic similarity between natural language sentences. However, we identify a critical limitation: BERTScore exhibits low sensitivity to numerical variation, a significant weakness in finance where numerical precision directly affects meaning (e.g., distinguishing a 2% gain from a 20% loss). We introduce FinNuE, a diagnostic dataset constructed with controlled numerical perturbations across earnings calls, regulatory filings, social media, and news articles. Using FinNuE, demonstrate that BERTScore fails to distinguish semantically critical numerical differences, often assigning high similarity scores to financially divergent text pairs. Our findings reveal fundamental limitations of embedding-based metrics for finance and motivate numerically-aware evaluation frameworks for financial NLP.
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            <a href="https://www.alphaxiv.org/abs/2511.10459v1" target="_blank" rel="noopener noreferrer">
                LocalBench：基于县级本地知识与推理的大语言模型基准测试
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            LocalBench: Benchmarking LLMs on County-Level Local Knowledge and Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihan Gao, Yifei Xu, Jacob Thebault-Spieker
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在特定地理知识上的基准测试和评估，属于纯粹的评估基准研究。虽然涉及LLM技术，但专注于地理知识测试而非推荐系统、搜索或广告的核心技术进展。论文没有展示在推荐、搜索或广告领域的直接应用潜力，主要属于NLP评估范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:26:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10459v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10459v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CY</span></div>
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                    Large language models (LLMs) have been widely evaluated on macro-scale geographic tasks, such as global factual recall, event summarization, and regional reasoning. Yet, their ability to handle hyper-local knowledge remains poorly understood. This gap is increasingly consequential as real-world applications, from civic platforms to community journalism, demand AI systems that can reason about neighborhood-specific dynamics, cultural narratives, and local governance. Existing benchmarks fall short in capturing this complexity, often relying on coarse-grained data or isolated references. We present LocalBench, the first benchmark designed to systematically evaluate LLMs on county-level local knowledge across the United States. Grounded in the Localness Conceptual Framework, LocalBench includes 14,782 validated question-answer pairs across 526 U.S. counties in 49 states, integrating diverse sources such as Census statistics, local subreddit discourse, and regional news. It spans physical, cognitive, and relational dimensions of locality. Using LocalBench, we evaluate 13 state-of-the-art LLMs under both closed-book and web-augmented settings. Our findings reveal critical limitations: even the best-performing models reach only 56.8% accuracy on narrative-style questions and perform below 15.5% on numerical reasoning. Moreover, larger model size and web augmentation do not guarantee better performance, for example, search improves Gemini's accuracy by +13.6%, but reduces GPT-series performance by -11.4%. These results underscore the urgent need for language models that can support equitable, place-aware AI systems: capable of engaging with the diverse, fine-grained realities of local communities across geographic and cultural contexts.
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            <a href="https://www.alphaxiv.org/abs/2511.10475v1" target="_blank" rel="noopener noreferrer">
                内在维度作为类别不平衡的无模型度量
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Çağrı Eser, Zeynep Sonat Baltacı, Emre Akbaş, Sinan Kalkan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究类别不平衡的度量方法，属于机器学习基础理论范畴。虽然类别不平衡问题在推荐系统和搜索中可能影响长尾物品的推荐效果，但该工作聚焦于理论度量而非具体的推荐、搜索或广告应用，也未涉及LLM、Transformer架构或异构数据建模等核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:41:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10475v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10475v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent differences in the learning difficulties of classes. Alternatively, one can use complex measures such as training loss and uncertainty, which, however, depend on training a machine learning model. Our paper proposes using data Intrinsic Dimensionality (ID) as an easy-to-compute, model-free measure of imbalance that can be seamlessly incorporated into various imbalance mitigation methods. Our results across five different datasets with a diverse range of imbalance ratios show that ID consistently outperforms cardinality-based re-weighting and re-sampling techniques used in the literature. Moreover, we show that combining ID with cardinality can further improve performance. Code: https://github.com/cagries/IDIM.
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            <a href="https://www.alphaxiv.org/abs/2511.10376v1" target="_blank" rel="noopener noreferrer">
                MSGNav：释放多模态3D场景图在零样本具身导航中的威力
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            MSGNav: Unleashing the Power of Multi-modal 3D Scene Graph for Zero-Shot Embodied Navigation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xun Huang, Shijia Zhao, Yunxiang Wang, Xin Lu, Wanfa Zhang, Rongsheng Qu, Weixin...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于具身导航和3D场景理解，属于机器人学和计算机视觉领域。虽然涉及多模态学习，但其核心应用（机器人导航）与推荐系统、搜索或广告没有直接关联。多模态3D场景图技术可能对处理空间数据的应用有间接启发，但缺乏明确的RecSys/Search/Ads应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:51:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10376v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10376v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Embodied navigation is a fundamental capability for robotic agents operating. Real-world deployment requires open vocabulary generalization and low training overhead, motivating zero-shot methods rather than task-specific RL training. However, existing zero-shot methods that build explicit 3D scene graphs often compress rich visual observations into text-only relations, leading to high construction cost, irreversible loss of visual evidence, and constrained vocabularies. To address these limitations, we introduce the Multi-modal 3D Scene Graph (M3DSG), which preserves visual cues by replacing textual relational edges with dynamically assigned images. Built on M3DSG, we propose MSGNav, a zero-shot navigation system that includes a Key Subgraph Selection module for efficient reasoning, an Adaptive Vocabulary Update module for open vocabulary support, and a Closed-Loop Reasoning module for accurate exploration reasoning. Additionally, we further identify the last-mile problem in zero-shot navigation - determining the feasible target location with a suitable final viewpoint, and propose a Visibility-based Viewpoint Decision module to explicitly resolve it. Comprehensive experimental results demonstrate that MSGNav achieves state-of-the-art performance on GOAT-Bench and HM3D-OVON datasets. The open-source code will be publicly available.
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            <a href="https://www.alphaxiv.org/abs/2511.10300v1" target="_blank" rel="noopener noreferrer">
                基于专家混合模型的卫星图像贫民窟可泛化检测
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            Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sumin Lee, Sungwon Park, Jeasurk Yang, Jihee Kim, Meeyoung Cha
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然论文涉及专家混合模型（MoE）这一Transformer架构相关技术，但其核心应用领域是卫星图像分析和地理空间检测，与推荐系统、搜索或广告的关联性极弱。贫民窟检测属于纯粹的计算机视觉应用，缺乏明确的RecSys/Search/Ads应用场景或数据模态类比。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:35:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10300v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10300v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CY</span></div>
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                    Satellite-based slum segmentation holds significant promise in generating global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, hindering the ability of models trained on specific regions to generalize effectively to unseen locations. To address this, we introduce a large-scale high-resolution dataset and propose GRAM (Generalized Region-Aware Mixture-of-Experts), a two-phase test-time adaptation framework that enables robust slum segmentation without requiring labeled data from target regions. We compile a million-scale satellite imagery dataset from 12 cities across four continents for source training. Using this dataset, the model employs a Mixture-of-Experts architecture to capture region-specific slum characteristics while learning universal features through a shared backbone. During adaptation, prediction consistency across experts filters out unreliable pseudo-labels, allowing the model to generalize effectively to previously unseen regions. GRAM outperforms state-of-the-art baselines in low-resource settings such as African cities, offering a scalable and label-efficient solution for global slum mapping and data-driven urban planning.
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            <a href="https://www.alphaxiv.org/abs/2511.10279v1" target="_blank" rel="noopener noreferrer">
                PROPA：通过强化学习实现视觉推理中的过程级优化
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            PROPA: Toward Process-level Optimization in Visual Reasoning via Reinforcement Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanbei Jiang, Chao Lei, Yihao Ding, Krista Ehinger, Jey Han Lau
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及强化学习，但专注于视觉推理这一计算机视觉领域，与推荐系统、搜索或广告没有明确的关联。强化学习在过程级优化方面的进展可能具有间接价值，但论文标题未表明任何在RecSys/Search/Ads领域的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:06:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10279v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10279v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite significant progress, Vision-Language Models (VLMs) still struggle with complex visual reasoning, where multi-step dependencies cause early errors to cascade through the reasoning chain. Existing post-training paradigms are limited: Supervised Fine-Tuning (SFT) relies on costly step-level annotations, while Reinforcement Learning with Verifiable Rewards (RLVR) methods like GRPO provide only sparse, outcome-level feedback, hindering stable optimization. We introduce PROPA (Process-level Reasoning Optimization with interleaved Policy Alignment), a novel framework that integrates Monte Carlo Tree Search (MCTS) with GRPO to generate dense, process-level rewards and optimize reasoning at each intermediate step without human annotations. To overcome the cold-start problem, PROPA interleaves GRPO updates with SFT, enabling the model to learn from both successful and failed reasoning trajectories. A Process Reward Model (PRM) is further trained to guide inference-time search, aligning the test-time search with the training signal. Across seven benchmarks and four VLM backbones, PROPA consistently outperforms both SFT- and RLVR-based baselines. It achieves up to 17.0% gains on in-domain tasks and 21.0% gains on out-of-domain tasks compared to existing state-of-the-art, establishing a strong reasoning and generalization capability for visual reasoning tasks. The code isavailable at: https://github.com/YanbeiJiang/PROPA.
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            <a href="https://www.alphaxiv.org/abs/2511.10211v1" target="_blank" rel="noopener noreferrer">
                HeatV2X：通过高效对齐与交互实现可扩展的异构协同感知
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            HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yueran Zhao, Zhang Zhang, Chao Sun, Tianze Wang, Chao Yue, Nuoran Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注车联网(V2X)中的协同感知问题，属于自动驾驶领域。虽然提到了异构数据对齐和交互，但这些技术主要针对传感器融合和车辆间通信，与推荐系统、搜索或广告中的异构数据处理没有直接关联。该工作的核心应用场景是自动驾驶感知，而非用户行为建模或内容排序。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:33:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10211v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10211v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission. However, as more agents participate, existing frameworks face two key challenges: (1) the participating agents are inherently multi-modal and heterogeneous, and (2) the collaborative framework must be scalable to accommodate new agents. The former requires effective cross-agent feature alignment to mitigate heterogeneity loss, while the latter renders full-parameter training impractical, highlighting the importance of scalable adaptation. To address these issues, we propose Heterogeneous Adaptation (HeatV2X), a scalable collaborative framework. We first train a high-performance agent based on heterogeneous graph attention as the foundation for collaborative learning. Then, we design Local Heterogeneous Fine-Tuning and Global Collaborative Fine-Tuning to achieve effective alignment and interaction among heterogeneous agents. The former efficiently extracts modality-specific differences using Hetero-Aware Adapters, while the latter employs the Multi-Cognitive Adapter to enhance cross-agent collaboration and fully exploit the fusion potential. These designs enable substantial performance improvement of the collaborative framework with minimal training cost. We evaluate our approach on the OPV2V-H and DAIR-V2X datasets. Experimental results demonstrate that our method achieves superior perception performance with significantly reduced training overhead, outperforming existing state-of-the-art approaches. Our implementation will be released soon.
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            <a href="https://www.alphaxiv.org/abs/2511.10203v1" target="_blank" rel="noopener noreferrer">
                VISTA：一种面向多智能体轨迹预测的视觉与意图感知社交注意力框架
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            VISTA: A Vision and Intent-Aware Social Attention Framework for Multi-Agent Trajectory Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stephane Da Silva Martins, Emanuel Aldea, Sylvie Le Hégarat-Mascle
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多智能体轨迹预测，属于机器人学和自动驾驶领域。虽然涉及注意力机制，但其核心应用场景（多智能体轨迹预测）与推荐系统、搜索或广告的关联性较弱。论文中的社交注意力框架可能对用户行为序列建模有启发，但缺乏明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:17:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10203v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10203v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.RO</span></div>
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                    Multi-agent trajectory prediction is crucial for autonomous systems operating in dense, interactive environments. Existing methods often fail to jointly capture agents' long-term goals and their fine-grained social interactions, which leads to unrealistic multi-agent futures. We propose VISTA, a recursive goal-conditioned transformer for multi-agent trajectory forecasting. VISTA combines (i) a cross-attention fusion module that integrates long-horizon intent with past motion, (ii) a social-token attention mechanism for flexible interaction modeling across agents, and (iii) pairwise attention maps that make social influence patterns interpretable at inference time. Our model turns single-agent goal-conditioned prediction into a coherent multi-agent forecasting framework. Beyond standard displacement metrics, we evaluate trajectory collision rates as a measure of joint realism. On the high-density MADRAS benchmark and on SDD, VISTA achieves state-of-the-art accuracy and substantially fewer collisions. On MADRAS, it reduces the average collision rate of strong baselines from 2.14 to 0.03 percent, and on SDD it attains zero collisions while improving ADE, FDE, and minFDE. These results show that VISTA generates socially compliant, goal-aware, and interpretable trajectories, making it promising for safety-critical autonomous systems.
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            <a href="https://www.alphaxiv.org/abs/2511.10094v1" target="_blank" rel="noopener noreferrer">
                我的模型如何失败？使用套娃转码器自动识别和解释物理合理性失效模式
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            How does My Model Fail? Automatic Identification and Interpretation of Physical Plausibility Failure Modes with Matryoshka Transcoders
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiming Tang, Abhijeet Sinha, Dianbo Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注物理合理性和失效模式分析，这属于特定领域（物理模拟）的模型诊断问题。虽然提到了自动识别技术，但没有明确连接到推荐系统、搜索或广告领域的应用场景。套娃转码器可能涉及某种多尺度处理，但论文焦点在物理合理性而非推荐系统相关的用户行为建模或内容理解。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:51:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10094v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10094v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Although recent generative models are remarkably capable of producing instruction-following and realistic outputs, they remain prone to notable physical plausibility failures. Though critical in applications, these physical plausibility errors often escape detection by existing evaluation methods. Furthermore, no framework exists for automatically identifying and interpreting specific physical error patterns in natural language, preventing targeted model improvements. We introduce Matryoshka Transcoders, a novel framework for the automatic discovery and interpretation of physical plausibility features in generative models. Our approach extends the Matryoshka representation learning paradigm to transcoder architectures, enabling hierarchical sparse feature learning at multiple granularity levels. By training on intermediate representations from a physical plausibility classifier and leveraging large multimodal models for interpretation, our method identifies diverse physics-related failure modes without manual feature engineering, achieving superior feature relevance and feature accuracy compared to existing approaches. We utilize the discovered visual patterns to establish a benchmark for evaluating physical plausibility in generative models. Our analysis of eight state-of-the-art generative models provides valuable insights into how these models fail to follow physical constraints, paving the way for further model improvements.
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            <a href="https://www.alphaxiv.org/abs/2511.09942v1" target="_blank" rel="noopener noreferrer">
                AdaptViG：具有指数衰减门控的自适应视觉图神经网络
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            AdaptViG: Adaptive Vision GNN with Exponential Decay Gating
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mustafa Munir, Md Mostafijur Rahman, Radu Marculescu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于视觉图神经网络（Vision GNN）的架构改进，属于计算机视觉领域的特定技术优化。虽然涉及图神经网络和自适应门控机制，但缺乏与推荐系统、搜索或广告领域的直接关联或潜在应用场景。该工作主要针对视觉任务，不符合当前关注的LLM赋能技术或异构数据统一建模等方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:16:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09942v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09942v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision Graph Neural Networks (ViGs) offer a new direction for advancements in vision architectures. While powerful, ViGs often face substantial computational challenges stemming from their graph construction phase, which can hinder their efficiency. To address this issue we propose AdaptViG, an efficient and powerful hybrid Vision GNN that introduces a novel graph construction mechanism called Adaptive Graph Convolution. This mechanism builds upon a highly efficient static axial scaffold and a dynamic, content-aware gating strategy called Exponential Decay Gating. This gating mechanism selectively weighs long-range connections based on feature similarity. Furthermore, AdaptViG employs a hybrid strategy, utilizing our efficient gating mechanism in the early stages and a full Global Attention block in the final stage for maximum feature aggregation. Our method achieves a new state-of-the-art trade-off between accuracy and efficiency among Vision GNNs. For instance, our AdaptViG-M achieves 82.6% top-1 accuracy, outperforming ViG-B by 0.3% while using 80% fewer parameters and 84% fewer GMACs. On downstream tasks, AdaptViG-M obtains 45.8 mIoU, 44.8 APbox, and 41.1 APmask, surpassing the much larger EfficientFormer-L7 by 0.7 mIoU, 2.2 APbox, and 2.1 APmask, respectively, with 78% fewer parameters.
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            <a href="https://www.alphaxiv.org/abs/2511.09909v1" target="_blank" rel="noopener noreferrer">
                模拟分布动态：面向单领域广义目标检测的液态时序特征演化
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            Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihao Zhang, Yang Li, Aming Wu, Yahong Han
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的目标检测任务，特别是领域泛化和时序特征演化问题。虽然提到了特征演化概念，但其核心应用场景是视觉目标检测而非推荐系统、搜索或广告领域。论文的技术内容与异构数据建模、Transformer架构改进或LLM应用没有明显关联，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:10:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09909v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09909v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In this paper, we focus on Single-Domain Generalized Object Detection (Single-DGOD), aiming to transfer a detector trained on one source domain to multiple unknown domains. Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity, thereby mitigating the lack of access to target domain data. However, in real-world scenarios such as changes in weather or lighting conditions, domain shifts often occur continuously and gradually. Discrete augmentations and static perturbations fail to effectively capture the dynamic variation of feature distributions, thereby limiting the model's ability to perceive fine-grained cross-domain differences. To this end, we propose a new method, Liquid Temporal Feature Evolution, which simulates the progressive evolution of features from the source domain to simulated latent distributions by incorporating temporal modeling and liquid neural network-driven parameter adjustment. Specifically, we introduce controllable Gaussian noise injection and multi-scale Gaussian blurring to simulate initial feature perturbations, followed by temporal modeling and a liquid parameter adjustment mechanism to generate adaptive modulation parameters, enabling a smooth and continuous adaptation across domains. By capturing progressive cross-domain feature evolution and dynamically regulating adaptation paths, our method bridges the source-unknown domain distribution gap, significantly boosting generalization and robustness to unseen shifts. Significant performance improvements on the Diverse Weather dataset and Real-to-Art benchmark demonstrate the superiority of our method. Our code is available at https://github.com/2490o/LTFE.
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            <a href="https://www.alphaxiv.org/abs/2511.10017v1" target="_blank" rel="noopener noreferrer">
                AffordBot：基于多模态大语言模型的3D细粒度具身推理
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            AffordBot: 3D Fine-grained Embodied Reasoning via Multimodal Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyi Wang, Xun Yang, Yanlong Xu, Yuchen Wu, Zhen Li, Na Zhao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D具身推理和机器人交互，属于纯粹的具身智能和机器人学领域。虽然涉及多模态大语言模型，但其应用场景（3D环境中的细粒度推理）与推荐系统、搜索或广告的核心技术需求没有直接关联，无法看出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:43:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10017v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10017v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Effective human-agent collaboration in physical environments requires understanding not only what to act upon, but also where the actionable elements are and how to interact with them. Existing approaches often operate at the object level or disjointedly handle fine-grained affordance reasoning, lacking coherent, instruction-driven grounding and reasoning. In this work, we introduce a new task: Fine-grained 3D Embodied Reasoning, which requires an agent to predict, for each referenced affordance element in a 3D scene, a structured triplet comprising its spatial location, motion type, and motion axis, based on a task instruction. To solve this task, we propose AffordBot, a novel framework that integrates Multimodal Large Language Models (MLLMs) with a tailored chain-of-thought (CoT) reasoning paradigm. To bridge the gap between 3D input and 2D-compatible MLLMs, we render surround-view images of the scene and project 3D element candidates into these views, forming a rich visual representation aligned with the scene geometry. Our CoT pipeline begins with an active perception stage, prompting the MLLM to select the most informative viewpoint based on the instruction, before proceeding with step-by-step reasoning to localize affordance elements and infer plausible interaction motions. Evaluated on the SceneFun3D dataset, AffordBot achieves state-of-the-art performance, demonstrating strong generalization and physically grounded reasoning with only 3D point cloud input and MLLMs.
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                超越余弦相似度：用于无参考图像质量评估的幅度感知CLIP
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            Beyond Cosine Similarity Magnitude-Aware CLIP for No-Reference Image Quality Assessment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhicheng Liao, Dongxu Wu, Zhenshan Shi, Sijie Mai, Hanwei Zhu, Lingyu Zhu, Yunch...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图像质量评估这一计算机视觉任务，虽然使用了CLIP模型，但其应用场景与推荐系统、搜索或广告的核心排序任务没有直接关联。图像质量评估主要用于图像处理和质量控制领域，无法直接应用于用户行为建模、内容推荐或搜索排序等核心业务场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:28:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09948v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09948v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent efforts have repurposed the Contrastive Language-Image Pre-training (CLIP) model for No-Reference Image Quality Assessment (NR-IQA) by measuring the cosine similarity between the image embedding and textual prompts such as "a good photo" or "a bad photo." However, this semantic similarity overlooks a critical yet underexplored cue: the magnitude of the CLIP image features, which we empirically find to exhibit a strong correlation with perceptual quality. In this work, we introduce a novel adaptive fusion framework that complements cosine similarity with a magnitude-aware quality cue. Specifically, we first extract the absolute CLIP image features and apply a Box-Cox transformation to statistically normalize the feature distribution and mitigate semantic sensitivity. The resulting scalar summary serves as a semantically-normalized auxiliary cue that complements cosine-based prompt matching. To integrate both cues effectively, we further design a confidence-guided fusion scheme that adaptively weighs each term according to its relative strength. Extensive experiments on multiple benchmark IQA datasets demonstrate that our method consistently outperforms standard CLIP-based IQA and state-of-the-art baselines, without any task-specific training.
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            <a href="https://www.alphaxiv.org/abs/2511.09933v1" target="_blank" rel="noopener noreferrer">
                面向对抗鲁棒行人重识别的去偏双不变性防御
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Debiased Dual-Invariant Defense for Adversarially Robust Person Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhang Zhou, Yanxiang Zhao, Zhongyun Hua, Zhipu Liu, Zhaoquan Gu, Qing Liao, Leo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于计算机视觉领域中的行人重识别任务，主要涉及对抗性防御和模型鲁棒性问题。虽然标题提到了'去偏'概念，但这是针对视觉模型对抗攻击的技术性去偏，而非推荐/搜索系统中的公平性或偏差问题。该工作属于纯粹的视觉应用领域，没有明显的推荐系统、搜索或广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:56:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09933v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09933v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking. However, advanced deep learning-based ReID models are highly susceptible to adversarial attacks, where imperceptible perturbations to pedestrian images can cause entirely incorrect predictions, posing significant security threats. Although numerous adversarial defense strategies have been proposed for classification tasks, their extension to metric learning tasks such as person ReID remains relatively unexplored. Moreover, the several existing defenses for person ReID fail to address the inherent unique challenges of adversarially robust ReID. In this paper, we systematically identify the challenges of adversarial defense in person ReID into two key issues: model bias and composite generalization requirements. To address them, we propose a debiased dual-invariant defense framework composed of two main phases. In the data balancing phase, we mitigate model bias using a diffusion-model-based data resampling strategy that promotes fairness and diversity in training data. In the bi-adversarial self-meta defense phase, we introduce a novel metric adversarial training approach incorporating farthest negative extension softening to overcome the robustness degradation caused by the absence of classifier. Additionally, we introduce an adversarially-enhanced self-meta mechanism to achieve dual-generalization for both unseen identities and unseen attack types. Experiments demonstrate that our method significantly outperforms existing state-of-the-art defenses.
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            <a href="https://www.alphaxiv.org/abs/2511.09868v1" target="_blank" rel="noopener noreferrer">
                记住我：通过三步仅推理衰减韧性策略弥合大型视觉语言模型中的长距离差距
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Remember Me: Bridging the Long-Range Gap in LVLMs with Three-Step Inference-Only Decay Resilience Strategies
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peng Gao, Yujian Lee, Xiaofeng Zhang, Zailong Chen, Hui Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉语言模型(VLMs)的长距离记忆问题，属于纯粹的视觉-语言多模态研究。虽然提到了'VLM Analogy for Heterogeneous Data'中的类比概念，但该工作主要解决VLM内部的技术挑战，没有明确展示在推荐系统、搜索或广告中的潜在应用。其技术方法(三步推理衰减策略)过于特定于VLM架构，缺乏向异构数据建模的直接可迁移性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 01:59:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09868v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09868v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large Vision-Language Models (LVLMs) have achieved impressive performance across a wide range of multimodal tasks. However, they still face critical challenges in modeling long-range dependencies under the usage of Rotary Positional Encoding (ROPE). Although it can facilitate precise modeling of token positions, it induces progressive attention decay as token distance increases, especially with progressive attention decay over distant token pairs, which severely impairs the model's ability to remember global context. To alleviate this issue, we propose inference-only Three-step Decay Resilience Strategies (T-DRS), comprising (1) Semantic-Driven DRS (SD-DRS), amplifying semantically meaningful but distant signals via content-aware residuals, (2) Distance-aware Control DRS (DC-DRS), which can purify attention by smoothly modulating weights based on positional distances, suppressing noise while preserving locality, and (3) re-Reinforce Distant DRS (reRD-DRS), consolidating the remaining informative remote dependencies to maintain global coherence. Together, the T-DRS recover suppressed long-range token pairs without harming local inductive biases. Extensive experiments on Vision Question Answering (VQA) benchmarks demonstrate that T-DRS can consistently improve performance in a training-free manner. The code can be accessed in https://github.com/labixiaoq-qq/Remember-me
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            <a href="https://www.alphaxiv.org/abs/2511.10212v1" target="_blank" rel="noopener noreferrer">
                用于多模态深度伪造检测与时序定位的下一帧特征预测
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Next-Frame Feature Prediction for Multimodal Deepfake Detection and Temporal Localization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ashutosh Anshul, Shreyas Gopal, Deepu Rajan, Eng Siong Chng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注深度伪造检测和时序定位，属于计算机视觉和多媒体安全领域。虽然涉及多模态数据和时序建模，但其核心应用场景（深度伪造检测）与推荐系统、搜索或广告的排名和用户建模需求没有直接关联。论文中的时序预测技术可能对用户行为序列建模有启发，但这种关联过于间接且微弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:34:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10212v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10212v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent multimodal deepfake detection methods designed for generalization conjecture that single-stage supervised training struggles to generalize across unseen manipulations and datasets. However, such approaches that target generalization require pretraining over real samples. Additionally, these methods primarily focus on detecting audio-visual inconsistencies and may overlook intra-modal artifacts causing them to fail against manipulations that preserve audio-visual alignment. To address these limitations, we propose a single-stage training framework that enhances generalization by incorporating next-frame prediction for both uni-modal and cross-modal features. Additionally, we introduce a window-level attention mechanism to capture discrepancies between predicted and actual frames, enabling the model to detect local artifacts around every frame, which is crucial for accurately classifying fully manipulated videos and effectively localizing deepfake segments in partially spoofed samples. Our model, evaluated on multiple benchmark datasets, demonstrates strong generalization and precise temporal localization.
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            <a href="https://www.alphaxiv.org/abs/2511.10074v1" target="_blank" rel="noopener noreferrer">
                VLF-MSC：基于视觉语言特征的多模态语义通信系统
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            <i class="fa fa-star mr-1"></i>2/10
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            VLF-MSC: Vision-Language Feature-Based Multimodal Semantic Communication System
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gwangyeon Ahn, Jiwan Seo, Joonhyuk Kang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉-语言模态的语义通信系统，属于通信技术领域而非推荐/搜索/广告的核心技术。虽然涉及多模态特征处理，但其应用场景偏向通信效率优化，与RecSys/Search/Ads的直接关联性较弱，仅在处理异构数据方面有潜在但遥远的借鉴意义。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:29:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10074v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10074v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">eess.SY</span></div>
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                    We propose Vision-Language Feature-based Multimodal Semantic Communication (VLF-MSC), a unified system that transmits a single compact vision-language representation to support both image and text generation at the receiver. Unlike existing semantic communication techniques that process each modality separately, VLF-MSC employs a pre-trained vision-language model (VLM) to encode the source image into a vision-language semantic feature (VLF), which is transmitted over the wireless channel. At the receiver, a decoder-based language model and a diffusion-based image generator are both conditioned on the VLF to produce a descriptive text and a semantically aligned image. This unified representation eliminates the need for modality-specific streams or retransmissions, improving spectral efficiency and adaptability. By leveraging foundation models, the system achieves robustness to channel noise while preserving semantic fidelity. Experiments demonstrate that VLF-MSC outperforms text-only and image-only baselines, achieving higher semantic accuracy for both modalities under low SNR with significantly reduced bandwidth.
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            <a href="https://www.alphaxiv.org/abs/2511.10047v1" target="_blank" rel="noopener noreferrer">
                MuSc-V2：基于无标签样本互评的零样本多模态工业异常分类与分割
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            MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xurui Li, Feng Xue, Yu Zhou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注工业异常检测，这是一个与计算机视觉和制造质量检测相关的领域。虽然提到了多模态和零样本学习，但这些技术主要应用于工业视觉异常检测，与推荐系统、搜索或广告的核心领域没有直接关联。该工作缺乏在RecSys/Search/Ads领域的潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:47:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10047v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10047v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: normal image patches across industrial products typically find many other similar patches, not only in 2D appearance but also in 3D shapes, while anomalies remain diverse and isolated. To explicitly leverage this discriminative property, we propose a Mutual Scoring framework (MuSc-V2) for zero-shot AC/AS, which flexibly supports single 2D/3D or multimodality. Specifically, our method begins by improving 3D representation through Iterative Point Grouping (IPG), which reduces false positives from discontinuous surfaces. Then we use Similarity Neighborhood Aggregation with Multi-Degrees (SNAMD) to fuse 2D/3D neighborhood cues into more discriminative multi-scale patch features for mutual scoring. The core comprises a Mutual Scoring Mechanism (MSM) that lets samples within each modality to assign score to each other, and Cross-modal Anomaly Enhancement (CAE) that fuses 2D and 3D scores to recover modality-specific missing anomalies. Finally, Re-scoring with Constrained Neighborhood (RsCon) suppresses false classification based on similarity to more representative samples. Our framework flexibly works on both the full dataset and smaller subsets with consistently robust performance, ensuring seamless adaptability across diverse product lines. In aid of the novel framework, MuSc-V2 achieves significant performance improvements: a $\textbf{+23.7\%}$ AP gain on the MVTec 3D-AD dataset and a $\textbf{+19.3\%}$ boost on the Eyecandies dataset, surpassing previous zero-shot benchmarks and even outperforming most few-shot methods. The code will be available at The code will be available at \href{https://github.com/HUST-SLOW/MuSc-V2}{https://github.com/HUST-SLOW/MuSc-V2}.
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            <a href="https://www.alphaxiv.org/abs/2511.10046v1" target="_blank" rel="noopener noreferrer">
                FreDFT：用于可见光-红外目标检测的频域融合Transformer
            </a>
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            FreDFT: Frequency Domain Fusion Transformer for Visible-Infrared Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wencong Wu, Xiuwei Zhang, Hanlin Yin, Shun Dai, Hongxi Zhang, Yanning Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及Transformer架构和多模态融合（可见光-红外），但其应用领域是计算机视觉中的目标检测，与推荐系统、搜索或广告没有直接关联。频域融合和跨模态建模的概念在理论上可能启发异构数据处理，但论文的具体应用场景（目标检测）与当前关注的核心领域相距甚远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:46:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10046v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10046v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Visible-infrared object detection has gained sufficient attention due to its detection performance in low light, fog, and rain conditions. However, visible and infrared modalities captured by different sensors exist the information imbalance problem in complex scenarios, which can cause inadequate cross-modal fusion, resulting in degraded detection performance. \textcolor{red}{Furthermore, most existing methods use transformers in the spatial domain to capture complementary features, ignoring the advantages of developing frequency domain transformers to mine complementary information.} To solve these weaknesses, we propose a frequency domain fusion transformer, called FreDFT, for visible-infrared object detection. The proposed approach employs a novel multimodal frequency domain attention (MFDA) to mine complementary information between modalities and a frequency domain feed-forward layer (FDFFL) via a mixed-scale frequency feature fusion strategy is designed to better enhance multimodal features. To eliminate the imbalance of multimodal information, a cross-modal global modeling module (CGMM) is constructed to perform pixel-wise inter-modal feature interaction in a spatial and channel manner. Moreover, a local feature enhancement module (LFEM) is developed to strengthen multimodal local feature representation and promote multimodal feature fusion by using various convolution layers and applying a channel shuffle. Extensive experimental results have verified that our proposed FreDFT achieves excellent performance on multiple public datasets compared with other state-of-the-art methods. The code of our FreDFT is linked at https://github.com/WenCongWu/FreDFT.
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            <a href="https://www.alphaxiv.org/abs/2511.10035v1" target="_blank" rel="noopener noreferrer">
                DGFusion：用于鲁棒多模态3D目标检测的双引导融合
            </a>
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            DGFusion: Dual-guided Fusion for Robust Multi-Modal 3D Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feiyang Jia, Caiyan Jia, Ailin Liu, Shaoqing Xu, Qiming Xia, Lin Liu, Lei Yang, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于多模态3D目标检测，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈关联度极低。虽然提及多模态融合技术，但其针对3D视觉任务的应用场景与文本/序列数据处理为主的推荐搜索领域缺乏直接关联，无法为异构数据建模提供可迁移的技术思路。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:18:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10035v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10035v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As a critical task in autonomous driving perception systems, 3D object detection is used to identify and track key objects, such as vehicles and pedestrians. However, detecting distant, small, or occluded objects (hard instances) remains a challenge, which directly compromises the safety of autonomous driving systems. We observe that existing multi-modal 3D object detection methods often follow a single-guided paradigm, failing to account for the differences in information density of hard instances between modalities. In this work, we propose DGFusion, based on the Dual-guided paradigm, which fully inherits the advantages of the Point-guide-Image paradigm and integrates the Image-guide-Point paradigm to address the limitations of the single paradigms. The core of DGFusion, the Difficulty-aware Instance Pair Matcher (DIPM), performs instance-level feature matching based on difficulty to generate easy and hard instance pairs, while the Dual-guided Modules exploit the advantages of both pair types to enable effective multi-modal feature fusion. Experimental results demonstrate that our DGFusion outperforms the baseline methods, with respective improvements of +1.0\% mAP, +0.8\% NDS, and +1.3\% average recall on nuScenes. Extensive experiments demonstrate consistent robustness gains for hard instance detection across ego-distance, size, visibility, and small-scale training scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.09955v1" target="_blank" rel="noopener noreferrer">
                基于视觉语言模型伪标签的每对象协同教学鲁棒目标检测
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        <div class="mb-2 text-base text-gray-700">
            Robust Object Detection with Pseudo Labels from VLMs using Per-Object Co-teaching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Uday Bhaskar, Rishabh Bhattacharya, Avinash Patel, Sarthak Khoche, Praveen Anil ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的目标检测任务，使用视觉语言模型生成伪标签，这属于纯粹的视觉领域研究。虽然提到了视觉语言模型，但应用场景和目标检测方法与推荐系统、搜索或广告的排序任务没有直接关联。论文的技术路线和问题设定都局限于视觉理解领域，缺乏向推荐/搜索/广告领域迁移的明确路径。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:37:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09955v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09955v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Foundation models, especially vision-language models (VLMs), offer compelling zero-shot object detection for applications like autonomous driving, a domain where manual labelling is prohibitively expensive. However, their detection latency and tendency to hallucinate predictions render them unsuitable for direct deployment. This work introduces a novel pipeline that addresses this challenge by leveraging VLMs to automatically generate pseudo-labels for training efficient, real-time object detectors. Our key innovation is a per-object co-teaching-based training strategy that mitigates the inherent noise in VLM-generated labels. The proposed per-object coteaching approach filters noisy bounding boxes from training instead of filtering the entire image. Specifically, two YOLO models learn collaboratively, filtering out unreliable boxes from each mini-batch based on their peers' per-object loss values. Overall, our pipeline provides an efficient, robust, and scalable approach to train high-performance object detectors for autonomous driving, significantly reducing reliance on costly human annotation. Experimental results on the KITTI dataset demonstrate that our method outperforms a baseline YOLOv5m model, achieving a significant mAP@0.5 boost ($31.12\%$ to $46.61\%$) while maintaining real-time detection latency. Furthermore, we show that supplementing our pseudo-labelled data with a small fraction of ground truth labels ($10\%$) leads to further performance gains, reaching $57.97\%$ mAP@0.5 on the KITTI dataset. We observe similar performance improvements for the ACDC and BDD100k datasets.
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            <a href="https://www.alphaxiv.org/abs/2511.09919v1" target="_blank" rel="noopener noreferrer">
                MosaicDoc：一个用于视觉丰富文档理解的大规模双语基准
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        <div class="mb-2 text-base text-gray-700">
            MosaicDoc: A Large-Scale Bilingual Benchmark for Visually Rich Document Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ketong Chen, Yuhao Chen, Yang Xue
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉文档理解基准，属于计算机视觉和文档分析领域，与推荐系统、搜索或广告的核心技术焦点关联较弱。虽然视觉丰富文档理解在电商搜索或广告素材分析中有潜在应用，但论文标题明确聚焦于基准创建而非直接的技术应用，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:34:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09919v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09919v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Despite the rapid progress of Vision-Language Models (VLMs), their capabilities are inadequately assessed by existing benchmarks, which are predominantly English-centric, feature simplistic layouts, and support limited tasks. Consequently, they fail to evaluate model performance for Visually Rich Document Understanding (VRDU), a critical challenge involving complex layouts and dense text. To address this, we introduce DocWeaver, a novel multi-agent pipeline that leverages Large Language Models to automatically generate a new benchmark. The result is MosaicDoc, a large-scale, bilingual (Chinese and English) resource designed to push the boundaries of VRDU. Sourced from newspapers and magazines, MosaicDoc features diverse and complex layouts (including multi-column and non-Manhattan), rich stylistic variety from 196 publishers, and comprehensive multi-task annotations (OCR, VQA, reading order, and localization). With 72K images and over 600K QA pairs, MosaicDoc serves as a definitive benchmark for the field. Our extensive evaluation of state-of-the-art models on this benchmark reveals their current limitations in handling real-world document complexity and charts a clear path for future research.
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            <a href="https://www.alphaxiv.org/abs/2511.09905v1" target="_blank" rel="noopener noreferrer">
                PRISM：通过解耦架构先验实现数据集蒸馏的多样化
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            PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Brian B. Moser, Shalini Strode, Federico Raue, Stanislav Frolov, Krzysztof Adamk...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注数据集蒸馏中的架构解耦问题，这属于通用的机器学习优化技术。虽然数据集蒸馏可能间接影响模型训练效率，但论文没有明确展示在推荐系统、搜索或广告领域的直接应用潜力。该工作更偏向通用深度学习优化，而非针对我们关注领域的核心进展或使能技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:06:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09905v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09905v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increases, this bias drives generation toward overly smooth, homogeneous samples, reducing intra-class diversity and limiting generalization. We present PRISM (PRIors from diverse Source Models), a framework that disentangles architectural priors during synthesis. PRISM decouples the logit-matching and regularization objectives, supervising them with different teacher architectures: a primary model for logits and a stochastic subset for batch-normalization (BN) alignment. On ImageNet-1K, PRISM consistently and reproducibly outperforms single-teacher methods (e.g., SRe2L) and recent multi-teacher variants (e.g., G-VBSM) at low- and mid-IPC regimes. The generated data also show significantly richer intra-class diversity, as reflected by a notable drop in cosine similarity between features. We further analyze teacher selection strategies (pre- vs. intra-distillation) and introduce a scalable cross-class batch formation scheme for fast parallel synthesis. Code will be released after the review period.
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            <a href="https://www.alphaxiv.org/abs/2511.09891v1" target="_blank" rel="noopener noreferrer">
                面向航拍图像微小目标检测的尺度感知中继与尺度自适应损失方法
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            Scale-Aware Relay and Scale-Adaptive Loss for Tiny Object Detection in Aerial Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinfu Li, Yuqi Huang, Hong Song, Ting Wang, Jianghan Xia, Yucong Lin, Jingfan Fa...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于计算机视觉中的微小目标检测技术，虽然涉及尺度适应这一通用挑战，但其应用场景（航拍图像）和核心方法（目标检测）与推荐系统、搜索和广告领域缺乏直接关联。即使考虑潜在的跨领域应用，该技术对RecSys/Search/Ads的适用性非常有限，主要因为推荐和搜索系统通常处理的是结构化数据和序列数据，而非视觉目标检测问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:54:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09891v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09891v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recently, despite the remarkable advancements in object detection, modern detectors still struggle to detect tiny objects in aerial images. One key reason is that tiny objects carry limited features that are inevitably degraded or lost during long-distance network propagation. Another is that smaller objects receive disproportionately greater regression penalties than larger ones during training. To tackle these issues, we propose a Scale-Aware Relay Layer (SARL) and a Scale-Adaptive Loss (SAL) for tiny object detection, both of which are seamlessly compatible with the top-performing frameworks. Specifically, SARL employs a cross-scale spatial-channel attention to progressively enrich the meaningful features of each layer and strengthen the cross-layer feature sharing. SAL reshapes the vanilla IoU-based losses so as to dynamically assign lower weights to larger objects. This loss is able to focus training on tiny objects while reducing the influence on large objects. Extensive experiments are conducted on three benchmarks (\textit{i.e.,} AI-TOD, DOTA-v2.0 and VisDrone2019), and the results demonstrate that the proposed method boosts the generalization ability by 5.5\% Average Precision (AP) when embedded in YOLOv5 (anchor-based) and YOLOx (anchor-free) baselines. Moreover, it also promotes the robust performance with 29.0\% AP on the real-world noisy dataset (\textit{i.e.,} AI-TOD-v2.0).
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            <a href="https://www.alphaxiv.org/abs/2511.10390v1" target="_blank" rel="noopener noreferrer">
                MonkeyOCR v1.5技术报告：为复杂模式解锁鲁棒性文档解析
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiarui Zhang, Yuliang Liu, Zijun Wu, Guosheng Pang, Zhili Ye, Yupei Zhong, Junte...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于OCR（光学字符识别）技术改进，属于文档解析和计算机视觉领域。虽然文档解析在搜索系统中可能有间接应用（如文档搜索），但该技术报告主要关注视觉文档处理，与推荐系统、广告或核心LLM/Transformer架构的直接关联性较弱，且未明确涉及用户行为建模或排名算法等核心RecSys/Search/Ads技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:12:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10390v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10390v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Document parsing is a core task in document intelligence, supporting applications such as information extraction, retrieval-augmented generation, and automated document analysis. However, real-world documents often feature complex layouts with multi-level tables, embedded images or formulas, and cross-page structures, which remain challenging for existing OCR systems. We introduce MonkeyOCR v1.5, a unified vision-language framework that enhances both layout understanding and content recognition through a two-stage parsing pipeline. The first stage employs a large multimodal model to jointly predict document layout and reading order, leveraging visual information to ensure structural and sequential consistency. The second stage performs localized recognition of text, formulas, and tables within detected regions, maintaining high visual fidelity while reducing error propagation. To address complex table structures, we propose a visual consistency-based reinforcement learning scheme that evaluates recognition quality via render-and-compare alignment, improving structural accuracy without manual annotations. Additionally, two specialized modules, Image-Decoupled Table Parsing and Type-Guided Table Merging, are introduced to enable reliable parsing of tables containing embedded images and reconstruction of tables crossing pages or columns. Comprehensive experiments on OmniDocBench v1.5 demonstrate that MonkeyOCR v1.5 achieves state-of-the-art performance, outperforming PPOCR-VL and MinerU 2.5 while showing exceptional robustness in visually complex document scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.10260v1" target="_blank" rel="noopener noreferrer">
                H3Former：基于超图的语义感知聚合，通过双曲层次对比损失实现细粒度视觉分类
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            H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongji Zhang, Siqi Li, Kuiyang Huang, Yue Gao, Yu Jiang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注细粒度视觉分类，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然论文涉及对比学习和层次结构建模，但这些技术在视觉分类中的应用与RecSys/Search/Ads的异构数据处理需求相距甚远，缺乏明确的跨模态应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:49:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10260v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10260v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations. Existing approaches typically rely on feature-selection mechanisms or region-proposal strategies to localize discriminative regions for semantic analysis. However, these methods often fail to capture discriminative cues comprehensively while introducing substantial category-agnostic redundancy. To address these limitations, we propose H3Former, a novel token-to-region framework that leverages high-order semantic relations to aggregate local fine-grained representations with structured region-level modeling. Specifically, we propose the Semantic-Aware Aggregation Module (SAAM), which exploits multi-scale contextual cues to dynamically construct a weighted hypergraph among tokens. By applying hypergraph convolution, SAAM captures high-order semantic dependencies and progressively aggregates token features into compact region-level representations. Furthermore, we introduce the Hyperbolic Hierarchical Contrastive Loss (HHCL), which enforces hierarchical semantic constraints in a non-Euclidean embedding space. The HHCL enhances inter-class separability and intra-class consistency while preserving the intrinsic hierarchical relationships among fine-grained categories. Comprehensive experiments conducted on four standard FGVC benchmarks validate the superiority of our H3Former framework.
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            <a href="https://www.alphaxiv.org/abs/2511.10560v1" target="_blank" rel="noopener noreferrer">
                OmniVGGT：全模态驱动的视觉几何基础模型
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            OmniVGGT: Omni-Modality Driven Visual Geometry Grounded
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haosong Peng, Hao Li, Yalun Dai, Yushi Lan, Yihang Luo, Tianyu Qi, Zhengshen Zha...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于视觉几何基础任务，属于计算机视觉领域。虽然提到了'全模态'概念，但核心是视觉几何基础，与推荐系统、搜索或广告的异构数据建模没有直接关联。该技术主要面向纯粹的视觉理解任务，在当前聚焦范围内相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:59:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10560v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10560v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    General 3D foundation models have started to lead the trend of unifying diverse vision tasks, yet most assume RGB-only inputs and ignore readily available geometric cues (e.g., camera intrinsics, poses, and depth maps). To address this issue, we introduce OmniVGGT, a novel framework that can effectively benefit from an arbitrary number of auxiliary geometric modalities during both training and inference. In our framework, a GeoAdapter is proposed to encode depth and camera intrinsics/extrinsics into a spatial foundation model. It employs zero-initialized convolutions to progressively inject geometric information without disrupting the foundation model's representation space. This design ensures stable optimization with negligible overhead, maintaining inference speed comparable to VGGT even with multiple additional inputs. Additionally, a stochastic multimodal fusion regimen is proposed, which randomly samples modality subsets per instance during training. This enables an arbitrary number of modality inputs during testing and promotes learning robust spatial representations instead of overfitting to auxiliary cues. Comprehensive experiments on monocular/multi-view depth estimation, multi-view stereo, and camera pose estimation demonstrate that OmniVGGT outperforms prior methods with auxiliary inputs and achieves state-of-the-art results even with RGB-only input. To further highlight its practical utility, we integrated OmniVGGT into vision-language-action (VLA) models. The enhanced VLA model by OmniVGGT not only outperforms the vanilla point-cloud-based baseline on mainstream benchmarks, but also effectively leverages accessible auxiliary inputs to achieve consistent gains on robotic tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.10334v1" target="_blank" rel="noopener noreferrer">
                学习区分：通过解耦语义对齐的弱监督视频异常检测
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        <div class="mb-2 text-base text-gray-700">
            Learning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenti Yin, Huaxin Zhang, Xiang Wang, Yuqing Lu, Yicheng Zhang, Bingquan Gong, Ji...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的视频异常检测，虽然涉及弱监督学习和语义对齐技术，但与推荐系统、搜索或广告的核心关注点没有直接关联。解耦表示学习在理论上可能对处理异构数据有启发，但论文的视觉异常检测应用场景与当前关注的LLM技术、Transformer架构或推荐系统应用缺乏明确的连接点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:06:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10334v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10334v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories. However, their objectives may tend to detect the most salient response segments, while neglecting to mine diverse normal patterns separated from anomalies, and are prone to category confusion due to similar appearance, leading to unsatisfactory fine-grained classification results. Therefore, we propose a novel Disentangled Semantic Alignment Network (DSANet) to explicitly separate abnormal and normal features from coarse-grained and fine-grained aspects, enhancing the distinguishability. Specifically, at the coarse-grained level, we introduce a self-guided normality modeling branch that reconstructs input video features under the guidance of learned normal prototypes, encouraging the model to exploit normality cues inherent in the video, thereby improving the temporal separation of normal patterns and anomalous events. At the fine-grained level, we present a decoupled contrastive semantic alignment mechanism, which first temporally decomposes each video into event-centric and background-centric components using frame-level anomaly scores and then applies visual-language contrastive learning to enhance class-discriminative representations. Comprehensive experiments on two standard benchmarks, namely XD-Violence and UCF-Crime, demonstrate that DSANet outperforms existing state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.10316v1" target="_blank" rel="noopener noreferrer">
                通过物理散焦建模与多视角几何监督实现深度一致的三维高斯溅射
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            Depth-Consistent 3D Gaussian Splatting via Physical Defocus Modeling and Multi-View Geometric Supervision
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu Deng, Baozhu Zhao, Junyan Su, Xiaohan Zhang, Qi Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D重建和计算机视觉技术，主要涉及3D高斯溅射和深度一致性方法。虽然3D建模在电商搜索的虚拟试穿等场景中有潜在应用，但论文本身并未直接涉及推荐系统、搜索或广告的核心技术，且与Transformer架构、LLM技术或异构数据统一建模等当前关注重点关联性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:51:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10316v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10316v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Three-dimensional reconstruction in scenes with extreme depth variations remains challenging due to inconsistent supervisory signals between near-field and far-field regions. Existing methods fail to simultaneously address inaccurate depth estimation in distant areas and structural degradation in close-range regions. This paper proposes a novel computational framework that integrates depth-of-field supervision and multi-view consistency supervision to advance 3D Gaussian Splatting. Our approach comprises two core components: (1) Depth-of-field Supervision employs a scale-recovered monocular depth estimator (e.g., Metric3D) to generate depth priors, leverages defocus convolution to synthesize physically accurate defocused images, and enforces geometric consistency through a novel depth-of-field loss, thereby enhancing depth fidelity in both far-field and near-field regions; (2) Multi-View Consistency Supervision employing LoFTR-based semi-dense feature matching to minimize cross-view geometric errors and enforce depth consistency via least squares optimization of reliable matched points. By unifying defocus physics with multi-view geometric constraints, our method achieves superior depth fidelity, demonstrating a 0.8 dB PSNR improvement over the state-of-the-art method on the Waymo Open Dataset. This framework bridges physical imaging principles and learning-based depth regularization, offering a scalable solution for complex depth stratification in urban environments.
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            <a href="https://www.alphaxiv.org/abs/2511.10309v1" target="_blank" rel="noopener noreferrer">
                CLIP4VI-ReID：通过CLIP语义桥学习模态共享表示用于可见光-红外行人重识别
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            CLIP4VI-ReID: Learning Modality-shared Representations via CLIP Semantic Bridge for Visible-Infrared Person Re-identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaomei Yang, Xizhan Gao, Sijie Niu, Fa Zhu, Guang Feng, Xiaofeng Qu, David Cama...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的行人重识别任务，使用CLIP处理可见光和红外两种视觉模态。虽然涉及多模态学习，但这是纯粹的视觉应用，没有明确指向推荐系统、搜索或广告领域的潜在应用。行人重识别属于安防监控领域，与当前关注的异构数据统一建模相关性较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:46:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10309v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10309v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper proposes a novel CLIP-driven modality-shared representation learning network named CLIP4VI-ReID for VI-ReID task, which consists of Text Semantic Generation (TSG), Infrared Feature Embedding (IFE), and High-level Semantic Alignment (HSA). Specifically, considering the huge gap in the physical characteristics between natural images and infrared images, the TSG is designed to generate text semantics only for visible images, thereby enabling preliminary visible-text modality alignment. Then, the IFE is proposed to rectify the feature embeddings of infrared images using the generated text semantics. This process injects id-related semantics into the shared image encoder, enhancing its adaptability to the infrared modality. Besides, with text serving as a bridge, it enables indirect visible-infrared modality alignment. Finally, the HSA is established to refine the high-level semantic alignment. This process ensures that the fine-tuned text semantics only contain id-related information, thereby achieving more accurate cross-modal alignment and enhancing the discriminability of the learned modal-shared representations. Extensive experimental results demonstrate that the proposed CLIP4VI-ReID achieves superior performance than other state-of-the-art methods on some widely used VI-ReID datasets.
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            <a href="https://www.alphaxiv.org/abs/2511.10292v1" target="_blank" rel="noopener noreferrer">
                大型视觉语言模型中低开销幻觉缓解的自适应残差更新引导
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengtao Zou, Ya Gao, Jiarui Guan, Bin Li, Pekka Marttinen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及大型视觉语言模型，但其核心焦点是幻觉缓解这一纯NLP中心主题，属于明确排除的无关主题。尽管提到了大型模型，但缺乏与推荐系统、搜索或广告领域的潜在应用联系，且幻觉缓解本身是纯粹的NLP评估问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:29:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10292v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10292v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating text inconsistent with visual inputs, which can critically undermine their reliability. Existing inference-time interventions to mitigate this issue present a challenging trade-off: while methods that steer internal states or adjust output logits can be effective, they often incur substantial computational overhead, typically requiring extra forward passes. This efficiency bottleneck can limit their practicality for real-world, latency-sensitive deployments. In this work, we aim to address this trade-off with Residual-Update Directed DEcoding Regulation (RUDDER), a low-overhead framework that steers LVLMs towards visually-grounded generation. RUDDER is built on two key innovations: (1) Contextual Activation Residual Direction (CARD) vector, a per-sample visual evidence vector extracted from the residual update of a self-attention layer during a single, standard forward pass. (2) A Bayesian-inspired adaptive gate that performs token-wise injection, applying a corrective signal whose strength is conditioned on the model's deviation from the visual context. Extensive experiments on key hallucination benchmarks, including POPE and CHAIR, indicate that RUDDER achieves performance comparable to state-of-the-art methods while introducing negligible computational latency, validating RUDDER as a pragmatic and effective approach for improving LVLMs' reliability without a significant compromise on efficiency.
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            <a href="https://www.alphaxiv.org/abs/2511.10241v1" target="_blank" rel="noopener noreferrer">
                TubeRMC：基于管道条件重建与互约束的弱监督时空视频定位
            </a>
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            TubeRMC: Tube-conditioned Reconstruction with Mutual Constraints for Weakly-supervised Spatio-Temporal Video Grounding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinxuan Li, Yi Zhang, Jian-Fang Hu, Chaolei Tan, Tianming Liang, Beihao Xia
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频时空定位的计算机视觉任务，主要涉及视频理解和弱监督学习。虽然视频推荐是推荐系统的一个子领域，但该论文的核心技术（视频管道重建和时空定位）与搜索、推荐或广告中的核心排名问题没有直接关联。其潜在的稀疏多模态建模思想可能适用于处理用户行为序列，但这种联系过于间接且不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:15:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10241v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10241v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Spatio-Temporal Video Grounding (STVG) aims to localize a spatio-temporal tube that corresponds to a given language query in an untrimmed video. This is a challenging task since it involves complex vision-language understanding and spatiotemporal reasoning. Recent works have explored weakly-supervised setting in STVG to eliminate reliance on fine-grained annotations like bounding boxes or temporal stamps. However, they typically follow a simple late-fusion manner, which generates tubes independent of the text description, often resulting in failed target identification and inconsistent target tracking. To address this limitation, we propose a Tube-conditioned Reconstruction with Mutual Constraints (\textbf{TubeRMC}) framework that generates text-conditioned candidate tubes with pre-trained visual grounding models and further refine them via tube-conditioned reconstruction with spatio-temporal constraints. Specifically, we design three reconstruction strategies from temporal, spatial, and spatio-temporal perspectives to comprehensively capture rich tube-text correspondences. Each strategy is equipped with a Tube-conditioned Reconstructor, utilizing spatio-temporal tubes as condition to reconstruct the key clues in the query. We further introduce mutual constraints between spatial and temporal proposals to enhance their quality for reconstruction. TubeRMC outperforms existing methods on two public benchmarks VidSTG and HCSTVG. Further visualization shows that TubeRMC effectively mitigates both target identification errors and inconsistent tracking.
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            <a href="https://www.alphaxiv.org/abs/2511.10134v1" target="_blank" rel="noopener noreferrer">
                基于上下文感知跨模态交互的显式时序语义建模用于密集视频描述
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            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingda Jia, Weiliang Meng, Zenghuang Fu, Yiheng Li, Qi Zeng, Yifan Zhang, Ju Xin...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的密集视频描述任务，主要涉及视频理解和跨模态交互技术。虽然论文提到了上下文感知和跨模态交互，但这些技术主要针对视觉-语言模态，与推荐系统、搜索或广告中的异构数据处理仅有微弱关联。该工作缺乏明确的RecSys/Search/Ads应用场景，属于纯粹的视觉-语言研究方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:48:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10134v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10134v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi-task architectures to achieve competitive performance. However, these pipelines rely on implicit modeling that uses frame-level or fragmented video features, failing to capture the temporal coherence across event sequences and comprehensive semantics within visual contexts. To address this, we propose an explicit temporal-semantic modeling framework called Context-Aware Cross-Modal Interaction (CACMI), which leverages both latent temporal characteristics within videos and linguistic semantics from text corpus. Specifically, our model consists of two core components: Cross-modal Frame Aggregation aggregates relevant frames to extract temporally coherent, event-aligned textual features through cross-modal retrieval; and Context-aware Feature Enhancement utilizes query-guided attention to integrate visual dynamics with pseudo-event semantics. Extensive experiments on the ActivityNet Captions and YouCook2 datasets demonstrate that CACMI achieves the state-of-the-art performance on dense video captioning task.
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            <a href="https://www.alphaxiv.org/abs/2511.10382v1" target="_blank" rel="noopener noreferrer">
                设计上的脆弱性：个性化生成中对抗防御的局限性
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            <i class="fa fa-star mr-1"></i>2/10
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            Fragile by Design: On the Limits of Adversarial Defenses in Personalized Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhen Chen, Yi Zhang, Xiangyu Yin, Chengxuan Qin, Xingyu Zhao, Xiaowei Huang, Wen...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注个性化生成中的对抗防御，这属于AIGC和内容生成领域，属于明确排除的无关主题。虽然个性化可能与推荐系统相关，但论文焦点是防御机制而非核心推荐算法或架构，因此相关性极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:56:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10382v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10382v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but also raise significant privacy concerns, particularly the risk of facial identity leakage. Recent defense mechanisms like Anti-DreamBooth attempt to mitigate this risk by injecting adversarial perturbations into user photos to prevent successful personalization. However, we identify two critical yet overlooked limitations of these methods. First, the adversarial examples often exhibit perceptible artifacts such as conspicuous patterns or stripes, making them easily detectable as manipulated content. Second, the perturbations are highly fragile, as even a simple, non-learned filter can effectively remove them, thereby restoring the model's ability to memorize and reproduce user identity. To investigate this vulnerability, we propose a novel evaluation framework, AntiDB_Purify, to systematically evaluate existing defenses under realistic purification threats, including both traditional image filters and adversarial purification. Results reveal that none of the current methods maintains their protective effectiveness under such threats. These findings highlight that current defenses offer a false sense of security and underscore the urgent need for more imperceptible and robust protections to safeguard user identity in personalized generation.
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            <a href="https://www.alphaxiv.org/abs/2511.10107v1" target="_blank" rel="noopener noreferrer">
                RobIA：面向深度立体视觉的鲁棒实例感知持续测试时自适应
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            RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jueun Ko, Hyewon Park, Hyesong Choi, Dongbo Min
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的深度立体视觉和测试时自适应技术，属于纯粹的视觉领域研究。虽然测试时自适应概念在理论上可能对推荐系统中的模型在线适应有所启发，但论文明确聚焦于立体视觉这一特定视觉任务，与推荐、搜索或广告系统的核心关注点缺乏直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:13:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10107v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10107v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-truth labels. While recent Test-Time Adaptation (TTA) methods offer promising solutions, most rely on static target domain assumptions and input-invariant adaptation strategies, limiting their effectiveness under continual shifts. In this paper, we propose RobIA, a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation (CTTA) in stereo depth estimation. RobIA integrates two key components: (1) Attend-and-Excite Mixture-of-Experts (AttEx-MoE), a parameter-efficient module that dynamically routes input to frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense pseudo-supervision by complementing sparse handcrafted labels. This strategy enables input-specific flexibility, broad supervision coverage, improving generalization under domain shift. Extensive experiments demonstrate that RobIA achieves superior adaptation performance across dynamic target domains while maintaining computational efficiency.
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            <a href="https://www.alphaxiv.org/abs/2511.10091v1" target="_blank" rel="noopener noreferrer">
                SUGAR：利用视觉-运动知识学习用于动作识别的骨架表示
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            SUGAR: Learning Skeleton Representation with Visual-Motion Knowledge for Action Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qilang Ye, Yu Zhou, Lian He, Jie Zhang, Xuanming Guo, Jiayu Zhang, Mingkui Tan, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的动作识别任务，使用骨架数据和视觉-运动知识，属于纯粹的视觉领域研究。虽然涉及多模态学习概念，但与推荐系统、搜索或广告的核心技术没有直接关联，也没有明确的Transformer架构改进或LLM应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:45:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10091v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10091v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action classification and description. However, when treating LLM as a recognizer, two questions arise: 1) How can LLMs understand skeleton? 2) How can LLMs distinguish among actions? To address these problems, we introduce a novel paradigm named learning Skeleton representation with visUal-motion knowledGe for Action Recognition (SUGAR). In our pipeline, we first utilize off-the-shelf large-scale video models as a knowledge base to generate visual, motion information related to actions. Then, we propose to supervise skeleton learning through this prior knowledge to yield discrete representations. Finally, we use the LLM with untouched pre-training weights to understand these representations and generate the desired action targets and descriptions. Notably, we present a Temporal Query Projection (TQP) module to continuously model the skeleton signals with long sequences. Experiments on several skeleton-based action classification benchmarks demonstrate the efficacy of our SUGAR. Moreover, experiments on zero-shot scenarios show that SUGAR is more versatile than linear-based methods.
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            <a href="https://www.alphaxiv.org/abs/2511.10088v1" target="_blank" rel="noopener noreferrer">
                eXIAA：用于对抗攻击的可解释性注入
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            <i class="fa fa-star mr-1"></i>2/10
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            eXIAA: eXplainable Injections for Adversarial Attack
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leonardo Pesce, Jiawen Wei, Gianmarco Mengaldo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注对抗攻击和可解释性，这属于模型安全领域，属于明确的无关主题。虽然对抗攻击在推荐系统或搜索中可能有防御应用，但论文焦点是攻击方法本身，而非在RecSys/Search/Ads中的直接应用或核心进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:42:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10088v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10088v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Post-hoc explainability methods are a subset of Machine Learning (ML) that aim to provide a reason for why a model behaves in a certain way. In this paper, we show a new black-box model-agnostic adversarial attack for post-hoc explainable Artificial Intelligence (XAI), particularly in the image domain. The goal of the attack is to modify the original explanations while being undetected by the human eye and maintain the same predicted class. In contrast to previous methods, we do not require any access to the model or its weights, but only to the model's computed predictions and explanations. Additionally, the attack is accomplished in a single step while significantly changing the provided explanations, as demonstrated by empirical evaluation. The low requirements of our method expose a critical vulnerability in current explainability methods, raising concerns about their reliability in safety-critical applications. We systematically generate attacks based on the explanations generated by post-hoc explainability methods (saliency maps, integrated gradients, and DeepLIFT SHAP) for pretrained ResNet-18 and ViT-B16 on ImageNet. The results show that our attacks could lead to dramatically different explanations without changing the predictive probabilities. We validate the effectiveness of our attack, compute the induced change based on the explanation with mean absolute difference, and verify the closeness of the original image and the corrupted one with the Structural Similarity Index Measure (SSIM).
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            <a href="https://www.alphaxiv.org/abs/2511.10020v1" target="_blank" rel="noopener noreferrer">
                Anomagic：跨模态提示驱动的零样本异常生成
            </a>
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            Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxin Jiang, Wei Luo, Hui Zhang, Qiyu Chen, Haiming Yao, Weiming Shen, Yunkang C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注跨模态提示驱动的异常生成，这主要属于异常检测和生成模型的范畴，与推荐系统、搜索或广告的核心技术关联较弱。虽然跨模态学习在理论上可以应用于处理异构数据，但论文的异常生成焦点使其在推荐/搜索/广告领域的直接应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:49:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10020v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10020v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting-based generation pipeline. A subsequent contrastive refinement strategy enforces precise alignment between synthesized anomalies and their masks, thereby bolstering downstream anomaly detection accuracy. To facilitate training, we introduce AnomVerse, a collection of 12,987 anomaly-mask-caption triplets assembled from 13 publicly available datasets, where captions are automatically generated by multimodal large language models using structured visual prompts and template-based textual hints. Extensive experiments demonstrate that Anomagic trained on AnomVerse can synthesize more realistic and varied anomalies than prior methods, yielding superior improvements in downstream anomaly detection. Furthermore, Anomagic can generate anomalies for any normal-category image using user-defined prompts, establishing a versatile foundation model for anomaly generation.
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            <a href="https://www.alphaxiv.org/abs/2511.09870v1" target="_blank" rel="noopener noreferrer">
                SAM-DAQ：基于深度引导自适应查询的Segment Anything模型用于RGB-D视频显著目标检测
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            SAM-DAQ: Segment Anything Model with Depth-guided Adaptive Queries for RGB-D Video Salient Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jia Lin, Xiaofei Zhou, Jiyuan Liu, Runmin Cong, Guodao Zhang, Zhi Liu, Jiyong Zh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的显著目标检测任务，特别是RGB-D视频模态。虽然Segment Anything Model (SAM)是基础模型，但该工作聚焦于视觉分割的特定应用场景，与推荐系统、搜索或广告的核心技术方向关联度较低。深度引导查询机制在推荐/搜索中的潜在应用场景有限，主要适用于视觉内容理解而非用户行为建模或排序任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:04:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09870v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09870v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recently segment anything model (SAM) has attracted widespread concerns, and it is often treated as a vision foundation model for universal segmentation. Some researchers have attempted to directly apply the foundation model to the RGB-D video salient object detection (RGB-D VSOD) task, which often encounters three challenges, including the dependence on manual prompts, the high memory consumption of sequential adapters, and the computational burden of memory attention. To address the limitations, we propose a novel method, namely Segment Anything Model with Depth-guided Adaptive Queries (SAM-DAQ), which adapts SAM2 to pop-out salient objects from videos by seamlessly integrating depth and temporal cues within a unified framework. Firstly, we deploy a parallel adapter-based multi-modal image encoder (PAMIE), which incorporates several depth-guided parallel adapters (DPAs) in a skip-connection way. Remarkably, we fine-tune the frozen SAM encoder under prompt-free conditions, where the DPA utilizes depth cues to facilitate the fusion of multi-modal features. Secondly, we deploy a query-driven temporal memory (QTM) module, which unifies the memory bank and prompt embeddings into a learnable pipeline. Concretely, by leveraging both frame-level queries and video-level queries simultaneously, the QTM module can not only selectively extract temporal consistency features but also iteratively update the temporal representations of the queries. Extensive experiments are conducted on three RGB-D VSOD datasets, and the results show that the proposed SAM-DAQ consistently outperforms state-of-the-art methods in terms of all evaluation metrics.
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            <a href="https://www.alphaxiv.org/abs/2511.10166v1" target="_blank" rel="noopener noreferrer">
                基于深度展开与可解释卷积的物理可解释多退化图像复原
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hu Gao, Xiaoning Lei, Xichen Xu, Depeng Dang, Lizhuang Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的图像复原任务，涉及物理可解释性和多退化处理。虽然深度展开和可解释卷积是技术上有趣的方法，但该工作明确聚焦于图像处理，与推荐系统、搜索或广告的核心技术栈没有直接关联。这些技术向推荐/搜索/广告领域的潜在迁移路径不明确，且论文本身没有表明这些应用方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 10:27:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10166v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10166v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Although image restoration has advanced significantly, most existing methods target only a single type of degradation. In real-world scenarios, images often contain multiple degradations simultaneously, such as rain, noise, and haze, requiring models capable of handling diverse degradation types. Moreover, methods that improve performance through module stacking often suffer from limited interpretability. In this paper, we propose a novel interpretability-driven approach for multi-degradation image restoration, built upon a deep unfolding network that maps the iterative process of a mathematical optimization algorithm into a learnable network structure. Specifically, we employ an improved second-order semi-smooth Newton algorithm to ensure that each module maintains clear physical interpretability. To further enhance interpretability and adaptability, we design an explainable convolution module inspired by the human brain's flexible information processing and the intrinsic characteristics of images, allowing the network to flexibly leverage learned knowledge and autonomously adjust parameters for different input. The resulting tightly integrated architecture, named InterIR, demonstrates excellent performance in multi-degradation restoration while remaining highly competitive on single-degradation tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.10142v1" target="_blank" rel="noopener noreferrer">
                Split-Layer：通过最大化特征空间维度增强隐式神经表示
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Split-Layer: Enhancing Implicit Neural Representation by Maximizing the Dimensionality of Feature Space
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhicheng Cai, Hao Zhu, Linsen Chen, Qiu Shen, Xun Cao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注隐式神经表示（INR）的架构改进，属于通用神经网络表示学习范畴。虽然特征空间维度最大化可能对某些表示学习任务有益，但该工作缺乏与推荐系统、搜索或广告领域的直接联系，也没有明确展示在Transformer架构或LLM技术中的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:55:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10142v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10142v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Implicit neural representation (INR) models signals as continuous functions using neural networks, offering efficient and differentiable optimization for inverse problems across diverse disciplines. However, the representational capacity of INR defined by the range of functions the neural network can characterize, is inherently limited by the low-dimensional feature space in conventional multilayer perceptron (MLP) architectures. While widening the MLP can linearly increase feature space dimensionality, it also leads to a quadratic growth in computational and memory costs. To address this limitation, we propose the split-layer, a novel reformulation of MLP construction. The split-layer divides each layer into multiple parallel branches and integrates their outputs via Hadamard product, effectively constructing a high-degree polynomial space. This approach significantly enhances INR's representational capacity by expanding the feature space dimensionality without incurring prohibitive computational overhead. Extensive experiments demonstrate that the split-layer substantially improves INR performance, surpassing existing methods across multiple tasks, including 2D image fitting, 2D CT reconstruction, 3D shape representation, and 5D novel view synthesis.
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            <a href="https://www.alphaxiv.org/abs/2511.10059v1" target="_blank" rel="noopener noreferrer">
                当视觉与听觉不一致时：多模态大语言模型能否辨别视听混淆？
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            <i class="fa fa-star mr-1"></i>2/10
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            When Eyes and Ears Disagree: Can MLLMs Discern Audio-Visual Confusion?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qilang Ye, Wei Zeng, Meng Liu, Jie Zhang, Yupeng Hu, Zitong Yu, Yu Zhou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多模态大语言模型在视听一致性判别上的能力，属于纯粹的跨模态理解研究。虽然涉及多模态建模，但其核心关注点（视听混淆检测）与推荐系统、搜索或广告中的异构数据处理缺乏直接关联，且未明确展示在RecSys/Search/Ads领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:59:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10059v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10059v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Can Multimodal Large Language Models (MLLMs) discern confused objects that are visually present but audio-absent? To study this, we introduce a new benchmark, AV-ConfuseBench, which simulates an ``Audio-Visual Confusion'' scene by modifying the corresponding sound of an object in the video, e.g., mute the sounding object and ask MLLMs Is there a/an muted-object sound''. Experimental results reveal that MLLMs, such as Qwen2.5-Omni and Gemini 2.5, struggle to discriminate non-existent audio due to visually dominated reasoning. Motivated by this observation, we introduce RL-CoMM, a Reinforcement Learning-based Collaborative Multi-MLLM that is built upon the Qwen2.5-Omni foundation. RL-CoMM includes two stages: 1) To alleviate visually dominated ambiguities, we introduce an external model, a Large Audio Language Model (LALM), as the reference model to generate audio-only reasoning. Then, we design a Step-wise Reasoning Reward function that enables MLLMs to self-improve audio-visual reasoning with the audio-only reference. 2) To ensure an accurate answer prediction, we introduce Answer-centered Confidence Optimization to reduce the uncertainty of potential heterogeneous reasoning differences. Extensive experiments on audio-visual question answering and audio-visual hallucination show that RL-CoMM improves the accuracy by 10~30\% over the baseline model with limited training data. Follow: https://github.com/rikeilong/AVConfusion.
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            <a href="https://www.alphaxiv.org/abs/2511.09878v1" target="_blank" rel="noopener noreferrer">
                RWKV-PCSSC：探索RWKV模型在点云语义场景补全中的应用
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            RWKV-PCSSC: Exploring RWKV Model for Point Cloud Semantic Scene Completion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenzhe He, Xiaojun Chen, Wentang Chen, Hongyu Wang, Ying Liu, Ruihui Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注点云处理和3D视觉任务，属于纯粹的视觉领域研究。虽然RWKV是一种高效的Transformer变体，但论文的应用场景（点云语义场景补全）与推荐系统、搜索或广告没有直接关联，且没有展示在异构数据处理或推荐相关任务中的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:22:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09878v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09878v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Semantic Scene Completion (SSC) aims to generate a complete semantic scene from an incomplete input. Existing approaches often employ dense network architectures with a high parameter count, leading to increased model complexity and resource demands. To address these limitations, we propose RWKV-PCSSC, a lightweight point cloud semantic scene completion network inspired by the Receptance Weighted Key Value (RWKV) mechanism. Specifically, we introduce a RWKV Seed Generator (RWKV-SG) module that can aggregate features from a partial point cloud to produce a coarse point cloud with coarse features. Subsequently, the point-wise feature of the point cloud is progressively restored through multiple stages of the RWKV Point Deconvolution (RWKV-PD) modules. By leveraging a compact and efficient design, our method achieves a lightweight model representation. Experimental results demonstrate that RWKV-PCSSC reduces the parameter count by 4.18$\times$ and improves memory efficiency by 1.37$\times$ compared to state-of-the-art methods PointSSC. Furthermore, our network achieves state-of-the-art performance on established indoor (SSC-PC, NYUCAD-PC) and outdoor (PointSSC) scene dataset, as well as on our proposed datasets (NYUCAD-PC-V2, 3D-FRONT-PC).
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            <a href="https://www.alphaxiv.org/abs/2511.10546v1" target="_blank" rel="noopener noreferrer">
                计算当代流派与文学小说的形式和制度边界
            </a>
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            Computing the Formal and Institutional Boundaries of Contemporary Genre and Literary Fiction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Natasha Johnson
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于文学流派和小说分类的学术分析，属于纯文学研究领域。内容完全不涉及推荐系统、搜索、广告或相关技术，也没有任何潜在的Transformer架构或LLM应用可能性。这是一个纯粹的文学理论研究，与所有关注的技术领域均无关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:47:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10546v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10546v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Though the concept of genre has been a subject of discussion for millennia, the relatively recent emergence of genre fiction has added a new layer to this ongoing conversation. While more traditional perspectives on genre have emphasized form, contemporary scholarship has invoked both formal and institutional characteristics in its taxonomy of genre, genre fiction, and literary fiction. This project uses computational methods to explore the soundness of genre as a formal designation as opposed to an institutional one. Pulling from Andrew Piper's CONLIT dataset of Contemporary Literature, we assemble a corpus of literary and genre fiction, with the latter category containing romance, mystery, and science fiction novels. We use Welch's ANOVA to compare the distribution of narrative features according to author gender within each genre and within genre versus literary fiction. Then, we use logistic regression to model the effect that each feature has on literary classification and to measure how author gender moderates these effects. Finally, we analyze stylistic and semantic vector representations of our genre categories to understand the importance of form and content in literary classification. This project finds statistically significant formal markers of each literary category and illustrates how female authorship narrows and blurs the target for achieving literary status.
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            <a href="https://www.alphaxiv.org/abs/2511.10519v1" target="_blank" rel="noopener noreferrer">
                换种说法：语言风格作为越狱向量
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        <div class="mb-2 text-base text-gray-700">
            Say It Differently: Linguistic Styles as Jailbreak Vectors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Srikant Panda, Avinash Rai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要研究LLM的越狱攻击和语言风格，属于安全性和对抗性攻击领域。这与我的关注点无关，因为我的重点排除了安全、隐私和纯粹NLP主题，如幻觉和评估基准。该研究没有展示在推荐系统、搜索或广告中的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:24:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10519v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10519v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are commonly evaluated for robustness against paraphrased or semantically equivalent jailbreak prompts, yet little attention has been paid to linguistic variation as an attack surface. In this work, we systematically study how linguistic styles such as fear or curiosity can reframe harmful intent and elicit unsafe responses from aligned models. We construct style-augmented jailbreak benchmark by transforming prompts from 3 standard datasets into 11 distinct linguistic styles using handcrafted templates and LLM-based rewrites, while preserving semantic intent. Evaluating 16 open- and close-source instruction-tuned models, we find that stylistic reframing increases jailbreak success rates by up to +57 percentage points. Styles such as fearful, curious and compassionate are most effective and contextualized rewrites outperform templated variants. To mitigate this, we introduce a style neutralization preprocessing step using a secondary LLM to strip manipulative stylistic cues from user inputs, significantly reducing jailbreak success rates. Our findings reveal a systemic and scaling-resistant vulnerability overlooked in current safety pipelines.
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            <a href="https://www.alphaxiv.org/abs/2511.10515v1" target="_blank" rel="noopener noreferrer">
                LOCA-R：在中国物理奥林匹克竞赛2025中实现接近完美的表现
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            LOCA-R: Near-Perfect Performance on the Chinese Physics Olympiad 2025
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dong-Shan Jian, Xiang Li, Chen-Xu Yan, Hui-Wen Zheng, Zhi-Zhang Bian, You-Le Fan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于物理学奥林匹克竞赛的特定领域应用，属于纯粹的物理学科竞赛任务。这完全属于被排除的领域特定应用范畴（医学、生物学、化学、物理学等），与推荐系统、搜索或广告的核心技术进展、LLM技术或Transformer架构没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:20:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10515v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10515v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">physics.ed-ph</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Olympiad-level physics problem-solving presents a significant challenge for both humans and artificial intelligence (AI), as it requires a sophisticated integration of precise calculation, abstract reasoning, and a fundamental grasp of physical principles. The Chinese Physics Olympiad (CPhO), renowned for its complexity and depth, serves as an ideal and rigorous testbed for these advanced capabilities. In this paper, we introduce LOCA-R (LOgical Chain Augmentation for Reasoning), an improved version of the LOCA framework adapted for complex reasoning, and apply it to the CPhO 2025 theory examination. LOCA-R achieves a near-perfect score of 313 out of 320 points, solidly surpassing the highest-scoring human competitor and significantly outperforming all baseline methods.
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            <a href="https://www.alphaxiv.org/abs/2511.10093v1" target="_blank" rel="noopener noreferrer">
                论大型语言模型在军事领域的应用
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            On the Military Applications of Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Satu Johansson, Taneli Riihonen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于军事领域的LLM应用，这属于明确的无关主题范畴。军事应用与推荐系统、搜索或广告领域没有任何技术关联，且军事领域属于被明确排除的特定领域应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:50:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10093v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10093v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this paper, military use cases or applications and implementation thereof are considered for natural language processing and large language models, which have broken into fame with the invention of the generative pre-trained transformer (GPT) and the extensive foundation model pretraining done by OpenAI for ChatGPT and others. First, we interrogate a GPT-based language model (viz. Microsoft Copilot) to make it reveal its own knowledge about their potential military applications and then critically assess the information. Second, we study how commercial cloud services (viz. Microsoft Azure) could be used readily to build such applications and assess which of them are feasible. We conclude that the summarization and generative properties of language models directly facilitate many applications at large and other features may find particular uses.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.10002v1" target="_blank" rel="noopener noreferrer">
                PustakAI：使用大型语言模型的课程对齐交互式教科书
            </a>
        </h3>
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        <div class="mb-2 text-base text-gray-700">
            PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shivam Sharma, Riya Naik, Tejas Gawas, Heramb Patil, Kunal Korgaonkar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于教育领域的LLM应用，涉及课程对齐和交互式教科书生成，属于纯粹的LLM应用场景。标题中没有任何内容表明与推荐系统、搜索或广告有潜在关联，完全属于被排除的AIGC和内容生成范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:12:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10002v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10002v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like content. This has revolutionized various sectors such as healthcare, software development, and education. In education, LLMs offer potential for personalized and interactive learning experiences, especially in regions with limited teaching resources. However, adapting these models effectively to curriculum-specific content, such as the National Council of Educational Research and Training (NCERT) syllabus in India, presents unique challenges in terms of accuracy, alignment, and pedagogical relevance. In this paper, we present the framework "PustakAI"\footnote{Pustak means `book' in many Indian languages.} for the design and evaluation of a novel question-answering dataset "NCERT-QA" aligned with the NCERT curriculum for English and Science subjects of grades 6 to 8. We classify the curated QA pairs as Factoid, Inferential, and Others (evaluative and reasoning). We evaluate the dataset with various prompting techniques, such as meta-prompt, few-shot, and CoT-style prompting, using diverse evaluation metrics to understand which approach aligns more efficiently with the structure and demands of the curriculum. Along with the usability of the dataset, we analyze the strengths and limitations of current open-source LLMs (Gemma3:1b, Llama3.2:3b, and Nemotron-mini:4b) and high-end LLMs (Llama-4-Scout-17B and Deepseek-r1-70B) as AI-based learning tools in formal education systems.
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            <a href="https://www.alphaxiv.org/abs/2511.10067v1" target="_blank" rel="noopener noreferrer">
                通过多层面自精炼学习增强大语言模型的医疗上下文感知能力
            </a>
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            Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxuan Zhou, Yubin Wang, Bin Wang, Chen Ning, Xien Liu, Ji Wu, Jianye Hao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的LLM应用，属于明确的无关主题范畴。虽然提到了上下文感知能力，但医疗领域的特定应用与搜索、推荐、广告等核心关注领域没有直接关联。论文的技术方法无法直接迁移到RecSys/Search/Ads场景中。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:13:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10067v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10067v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) have shown great promise in the medical domain, achieving strong performance on several benchmarks. However, they continue to underperform in real-world medical scenarios, which often demand stronger context-awareness, i.e., the ability to recognize missing or critical details (e.g., user identity, medical history, risk factors) and provide safe, helpful, and contextually appropriate responses. To address this issue, we propose Multifaceted Self-Refinement (MuSeR), a data-driven approach that enhances LLMs' context-awareness along three key facets (decision-making, communication, and safety) through self-evaluation and refinement. Specifically, we first design a attribute-conditioned query generator that simulates diverse real-world user contexts by varying attributes such as role, geographic region, intent, and degree of information ambiguity. An LLM then responds to these queries, self-evaluates its answers along three key facets, and refines its responses to better align with the requirements of each facet. Finally, the queries and refined responses are used for supervised fine-tuning to reinforce the model's context-awareness ability. Evaluation results on the latest HealthBench dataset demonstrate that our method significantly improves LLM performance across multiple aspects, with particularly notable gains in the context-awareness axis. Furthermore, by incorporating knowledge distillation with the proposed method, the performance of a smaller backbone LLM (e.g., Qwen3-32B) surpasses its teacher model, achieving a new SOTA across all open-source LLMs on HealthBench (63.8%) and its hard subset (43.1%). Code and dataset will be released at https://muser-llm.github.io.
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            <a href="https://www.alphaxiv.org/abs/2511.10070v1" target="_blank" rel="noopener noreferrer">
                ADI-20：阿拉伯语方言识别数据集与模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            ADI-20: Arabic Dialect Identification dataset and models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haroun Elleuch, Salima Mdhaffar, Yannick Estève, Fethi Bougares
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于阿拉伯语方言识别，属于特定语言的自然语言处理任务，与推荐系统、搜索或广告的核心技术领域没有直接关联。方言识别技术主要应用于语言学研究或特定地域的NLP应用，不具备在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:17:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10070v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10070v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    We present ADI-20, an extension of the previously published ADI-17 Arabic Dialect Identification (ADI) dataset. ADI-20 covers all Arabic-speaking countries' dialects. It comprises 3,556 hours from 19 Arabic dialects in addition to Modern Standard Arabic (MSA). We used this dataset to train and evaluate various state-of-the-art ADI systems. We explored fine-tuning pre-trained ECAPA-TDNN-based models, as well as Whisper encoder blocks coupled with an attention pooling layer and a classification dense layer. We investigated the effect of (i) training data size and (ii) the model's number of parameters on identification performance. Our results show a small decrease in F1 score while using only 30% of the original training data. We open-source our collected data and trained models to enable the reproduction of our work, as well as support further research in ADI.
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            <a href="https://www.alphaxiv.org/abs/2511.09893v1" target="_blank" rel="noopener noreferrer">
                面向临床相关医学图像描述的局部注意力增强型Swin Transformer
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        <div class="mb-2 text-base text-gray-700">
            Regional Attention-Enhanced Swin Transformer for Clinically Relevant Medical Image Captioning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zubia Naz, Farhan Asghar, Muhammad Ishfaq Hussain, Yahya Hadadi, Muhammad Aasim ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像描述生成，属于医疗领域的特定应用，与推荐系统、搜索或广告无关。虽然提到了Transformer架构和注意力机制，但其应用场景（医学图像）和任务（图像描述）均不在当前关注范围内，且没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:55:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09893v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09893v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automated medical image captioning translates complex radiological images into diagnostic narratives that can support reporting workflows. We present a Swin-BART encoder-decoder system with a lightweight regional attention module that amplifies diagnostically salient regions before cross-attention. Trained and evaluated on ROCO, our model achieves state-of-the-art semantic fidelity while remaining compact and interpretable. We report results as mean$\pm$std over three seeds and include $95\%$ confidence intervals. Compared with baselines, our approach improves ROUGE (proposed 0.603, ResNet-CNN 0.356, BLIP2-OPT 0.255) and BERTScore (proposed 0.807, BLIP2-OPT 0.645, ResNet-CNN 0.623), with competitive BLEU, CIDEr, and METEOR. We further provide ablations (regional attention on/off and token-count sweep), per-modality analysis (CT/MRI/X-ray), paired significance tests, and qualitative heatmaps that visualize the regions driving each description. Decoding uses beam search (beam size $=4$), length penalty $=1.1$, $no\_repeat\_ngram\_size$ $=3$, and max length $=128$. The proposed design yields accurate, clinically phrased captions and transparent regional attributions, supporting safe research use with a human in the loop.
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            <a href="https://www.alphaxiv.org/abs/2511.10583v1" target="_blank" rel="noopener noreferrer">
                使用MedGemma评估医疗订单提取的提示策略
            </a>
        </h3>
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            Evaluating Prompting Strategies with MedGemma for Medical Order Extraction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abhinand Balachandran, Bavana Durgapraveen, Gowsikkan Sikkan Sudhagar, Vidhya Va...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的订单提取，这属于明确的无关主题（医疗应用）。虽然提到了提示策略，但核心应用场景是医疗而非推荐系统、搜索或广告领域。论文内容与当前关注的技术领域没有任何直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:22:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10583v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10583v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The accurate extraction of medical orders from doctor-patient conversations is a critical task for reducing clinical documentation burdens and ensuring patient safety. This paper details our team submission to the MEDIQA-OE-2025 Shared Task. We investigate the performance of MedGemma, a new domain-specific open-source language model, for structured order extraction. We systematically evaluate three distinct prompting paradigms: a straightforward one-Shot approach, a reasoning-focused ReAct framework, and a multi-step agentic workflow. Our experiments reveal that while more complex frameworks like ReAct and agentic flows are powerful, the simpler one-shot prompting method achieved the highest performance on the official validation set. We posit that on manually annotated transcripts, complex reasoning chains can lead to "overthinking" and introduce noise, making a direct approach more robust and efficient. Our work provides valuable insights into selecting appropriate prompting strategies for clinical information extraction in varied data conditions.
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            <a href="https://www.alphaxiv.org/abs/2511.10287v1" target="_blank" rel="noopener noreferrer">
                OutSafe-Bench：大型语言模型中多模态攻击性内容检测的基准
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            OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuping Yan, Yuhan Xie, Yuanshuai Li, Yingchao Yu, Lingjuan Lyu, Yaochu Jin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM安全性和攻击性内容检测的基准测试，这属于评估基准和安全性主题，明确列在无关主题中。虽然涉及多模态内容，但核心关注点是内容安全而非推荐系统、搜索或广告的应用，没有展示出在这些领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:18:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10287v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10287v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Since Multimodal Large Language Models (MLLMs) are increasingly being integrated into everyday tools and intelligent agents, growing concerns have arisen regarding their possible output of unsafe contents, ranging from toxic language and biased imagery to privacy violations and harmful misinformation. Current safety benchmarks remain highly limited in both modality coverage and performance evaluations, often neglecting the extensive landscape of content safety. In this work, we introduce OutSafe-Bench, the first most comprehensive content safety evaluation test suite designed for the multimodal era. OutSafe-Bench includes a large-scale dataset that spans four modalities, featuring over 18,000 bilingual (Chinese and English) text prompts, 4,500 images, 450 audio clips and 450 videos, all systematically annotated across nine critical content risk categories. In addition to the dataset, we introduce a Multidimensional Cross Risk Score (MCRS), a novel metric designed to model and assess overlapping and correlated content risks across different categories. To ensure fair and robust evaluation, we propose FairScore, an explainable automated multi-reviewer weighted aggregation framework. FairScore selects top-performing models as adaptive juries, thereby mitigating biases from single-model judgments and enhancing overall evaluation reliability. Our evaluation of nine state-of-the-art MLLMs reveals persistent and substantial safety vulnerabilities, underscoring the pressing need for robust safeguards in MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.10120v1" target="_blank" rel="noopener noreferrer">
                泛化至未见过的灾害事件：一种因果视角
            </a>
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            Generalizing to Unseen Disaster Events: A Causal View
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Philipp Seeberger, Steffen Freisinger, Tobias Bocklet, Korbinian Riedhammer
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于灾害事件的泛化问题，属于特定领域应用，与推荐系统、搜索或广告的核心技术进展无关。因果视角虽然具有方法论价值，但论文明确针对灾害事件这一无关领域，缺乏对RecSys/Search/Ads的潜在应用连接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:29:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10120v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10120v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insights requires real-time processing of vast amounts of data. A major challenge in existing systems is their exposure to event-related biases, which negatively affects their ability to generalize to emerging events. While recent advancements in debiasing and causal learning offer promising solutions, they remain underexplored in the disaster event domain. In this work, we approach bias mitigation through a causal lens and propose a method to reduce event- and domain-related biases, enhancing generalization to future events. Our approach outperforms multiple baselines by up to +1.9% F1 and significantly improves a PLM-based classifier across three disaster classification tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.10090v1" target="_blank" rel="noopener noreferrer">
                ELYADATA与LIA在NADI 2025：自动语音识别与阿拉伯语方言识别子任务
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            ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haroun Elleuch, Youssef Saidi, Salima Mdhaffar, Yannick Estève, Fethi Bougares
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向语音识别(ASR)和方言识别(ADI)任务，属于纯粹的语音处理领域。这些技术没有显示出与推荐系统、搜索或广告的潜在应用关联，完全落在指定的不相关主题范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:44:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10090v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10090v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper describes Elyadata \& LIA's joint submission to the NADI multi-dialectal Arabic Speech Processing 2025. We participated in the Spoken Arabic Dialect Identification (ADI) and multi-dialectal Arabic ASR subtasks. Our submission ranked first for the ADI subtask and second for the multi-dialectal Arabic ASR subtask among all participants. Our ADI system is a fine-tuned Whisper-large-v3 encoder with data augmentation. This system obtained the highest ADI accuracy score of \textbf{79.83\%} on the official test set. For multi-dialectal Arabic ASR, we fine-tuned SeamlessM4T-v2 Large (Egyptian variant) separately for each of the eight considered dialects. Overall, we obtained an average WER and CER of \textbf{38.54\%} and \textbf{14.53\%}, respectively, on the test set. Our results demonstrate the effectiveness of large pre-trained speech models with targeted fine-tuning for Arabic speech processing.
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            <a href="https://www.alphaxiv.org/abs/2511.09915v1" target="_blank" rel="noopener noreferrer">
                HI-TransPA：听力障碍翻译个人助手
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            HI-TransPA: Hearing Impairments Translation Personal Assistant
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiming Ma, Shiyu Gan, Junhao Zhao, Xianming Li, Qingyun Pan, Peidong Wang, Ming...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于听力障碍辅助技术，属于医疗健康领域的特定应用。这完全超出了您关注的推荐系统、搜索、广告以及相关LLM/Transformer技术范畴，没有任何技术要素与您的核心研究方向相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 03:27:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09915v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09915v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.MM</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    To provide a unified and flexible solution for daily communication among hearing-impaired individuals, we introduce the Omni-Model paradigm into assistive technology and present HI-TransPA, an instruction-driven audio-visual personal assistant. The model fuses indistinct speech with high-frame-rate lip dynamics, enabling both translation and dialogue within a single multimodal framework. To tackle the challenges of noisy and heterogeneous raw data and the limited adaptability of existing Omni-Models to hearing-impaired speech, we construct a comprehensive preprocessing and curation pipeline that detects facial landmarks, isolates and stabilizes the lip region, and quantitatively assesses multimodal sample quality. These quality scores guide a curriculum learning strategy that first trains on clean, high-confidence samples and progressively incorporates harder cases to strengthen model robustness. We further adopt a SigLIP encoder combined with a Unified 3D-Resampler to efficiently encode high-frame-rate lip motion. Experiments on our purpose-built HI-Dialogue dataset show that HI-TransPA achieves state-of-the-art performance in both literal accuracy and semantic fidelity. This work establishes a foundation for applying Omni-Models to assistive communication technology, providing an end-to-end modeling framework and essential processing tools for future research.
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            <a href="https://www.alphaxiv.org/abs/2511.10615v1" target="_blank" rel="noopener noreferrer">
                面向轻量化视觉语言模型和定制化大语言模型评估的盲人与低视力可访问性研究
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            Towards Blind and Low-Vision Accessibility of Lightweight VLMs and Custom LLM-Evals
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shruti Singh Baghel, Yash Pratap Singh Rathore, Sushovan Jena, Anurag Pradhan, A...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型和LLM评估的可访问性应用，属于特定领域（辅助技术）的应用研究。虽然涉及VLM和LLM技术，但焦点是盲人和低视力人群的可访问性，这与搜索、推荐或广告系统的核心技术进步、架构改进或直接应用无关。该工作属于医疗/辅助技术领域，属于明确的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:45:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10615v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10615v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                    Large Vision-Language Models (VLMs) excel at understanding and generating video descriptions but their high memory, computation, and deployment demands hinder practical use particularly for blind and low-vision (BLV) users who depend on detailed, context-aware descriptions. To study the effect of model size on accessibility-focused description quality, we evaluate SmolVLM2 variants with 500M and 2.2B parameters across two diverse datasets: AVCaps (outdoor), and Charades (indoor). In this work, we introduce two novel evaluation frameworks specifically designed for BLV accessibility assessment: the Multi-Context BLV Framework evaluating spatial orientation, social interaction, action events, and ambience contexts; and the Navigational Assistance Framework focusing on mobility-critical information. Additionally, we conduct a systematic evaluation of four different prompt design strategies and deploy both models on a smartphone, evaluating FP32 and INT8 precision variants to assess real-world performance constraints on resource-limited mobile devices.
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            <a href="https://www.alphaxiv.org/abs/2511.10591v1" target="_blank" rel="noopener noreferrer">
                用于伤口护理视觉问答的挖掘提示与元数据引导生成
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            Mined Prompting and Metadata-Guided Generation for Wound Care Visual Question Answering
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bavana Durgapraveen, Sornaraj Sivasankaran, Abhinand Balachandran, Sriram Rajkum...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的视觉问答应用，具体针对伤口护理场景，这属于明确的医学领域应用。虽然涉及多模态技术，但其医疗应用场景与推荐系统、搜索或广告领域完全无关，且不包含任何可能应用于这些领域的通用技术进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:28:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10591v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10591v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    The rapid expansion of asynchronous remote care has intensified provider workload, creating demand for AI systems that can assist clinicians in managing patient queries more efficiently. The MEDIQA-WV 2025 shared task addresses this challenge by focusing on generating free-text responses to wound care queries paired with images. In this work, we present two complementary approaches developed for the English track. The first leverages a mined prompting strategy, where training data is embedded and the top-k most similar examples are retrieved to serve as few-shot demonstrations during generation. The second approach builds on a metadata ablation study, which identified four metadata attributes that consistently enhance response quality. We train classifiers to predict these attributes for test cases and incorporate them into the generation pipeline, dynamically adjusting outputs based on prediction confidence. Experimental results demonstrate that mined prompting improves response relevance, while metadata-guided generation further refines clinical precision. Together, these methods highlight promising directions for developing AI-driven tools that can provide reliable and efficient wound care support.
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            <a href="https://www.alphaxiv.org/abs/2511.10573v1" target="_blank" rel="noopener noreferrer">
                迈向情感智能与负责任的强化学习
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            Towards Emotionally Intelligent and Responsible Reinforcement Learning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Garapati Keerthana, Manik Gupta
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于强化学习的情感智能和负责任方面，这属于被明确排除的无关主题（RL论文与RecSys/Search/Ads无明确相关性）。标题中提到的'负责任'暗示了伦理考量，而'情感智能'与核心推荐/搜索/广告系统的技术进展无关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:09:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10573v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10573v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.MA</span></div>
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                    Personalized decision systems in healthcare and behavioral support often rely on static rule-based or engagement-maximizing heuristics that overlook users' emotional context and ethical constraints. Such approaches risk recommending insensitive or unsafe interventions, especially in domains involving serious mental illness, substance use disorders, or depression. To address this limitation, we propose a Responsible Reinforcement Learning (RRL) framework that integrates emotional and contextual understanding with ethical considerations into the sequential decision-making process. RRL formulates personalization as a Constrained Markov Decision Process (CMDP), where the agent optimizes engagement and adherence while ensuring emotional alignment and ethical safety. We introduce a multi-objective reward function that explicitly balances short-term behavioral engagement with long-term user well-being, and define an emotion-informed state representation that captures fluctuations in emotional readiness, affect, and risk. The proposed architecture can be instantiated with any RL algorithm (e.g., DQN, PPO) augmented with safety constraints or Lagrangian regularization. Conceptually, this framework operationalizes empathy and responsibility within machine learning policy optimization, bridging safe RL, affective computing and responsible AI. We discuss the implications of this approach for human-centric domains such as behavioral health, education, and digital therapeutics, and outline simulation-based validation paths for future empirical work. This paper aims to initiate a methodological conversation about ethically aligned reinforcement learning for emotionally aware and trustworthy personalization systems.
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            <a href="https://www.alphaxiv.org/abs/2511.10281v1" target="_blank" rel="noopener noreferrer">
                FactGuard：基于事件中心和常识引导的虚假新闻检测
            </a>
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            FactGuard: Event-Centric and Commonsense-Guided Fake News Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jing He, Han Zhang, Yuanhui Xiao, Wei Guo, Shaowen Yao, Renyang Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于虚假新闻检测，属于内容安全验证领域，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及内容理解，但主要解决信息可信度问题，而非排名、个性化或用户行为建模等核心RecSys/Search/Ads任务。该研究缺乏对推荐、搜索或广告系统的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:11:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10281v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10281v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Fake news detection methods based on writing style have achieved remarkable progress. However, as adversaries increasingly imitate the style of authentic news, the effectiveness of such approaches is gradually diminishing. Recent research has explored incorporating large language models (LLMs) to enhance fake news detection. Yet, despite their transformative potential, LLMs remain an untapped goldmine for fake news detection, with their real-world adoption hampered by shallow functionality exploration, ambiguous usability, and prohibitive inference costs. In this paper, we propose a novel fake news detection framework, dubbed FactGuard, that leverages LLMs to extract event-centric content, thereby reducing the impact of writing style on detection performance. Furthermore, our approach introduces a dynamic usability mechanism that identifies contradictions and ambiguous cases in factual reasoning, adaptively incorporating LLM advice to improve decision reliability. To ensure efficiency and practical deployment, we employ knowledge distillation to derive FactGuard-D, enabling the framework to operate effectively in cold-start and resource-constrained scenarios. Comprehensive experiments on two benchmark datasets demonstrate that our approach consistently outperforms existing methods in both robustness and accuracy, effectively addressing the challenges of style sensitivity and LLM usability in fake news detection.
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            <a href="https://www.alphaxiv.org/abs/2511.09843v1" target="_blank" rel="noopener noreferrer">
                CORONA-Fields：利用基础模型进行太阳风现象分类
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            CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniela Martin, Jinsu Hong, Connor O'Brien, Valmir P Moraes Filho, Jasmine R. Ko...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及太阳风现象分类，属于物理学和空间科学领域，与推荐系统、搜索或广告完全无关。基础模型在此处应用于特定科学领域，没有明显的跨领域应用潜力到商业推荐或搜索场景中。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 00:56:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09843v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09843v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">astro-ph.IM</span><span class="category-tag">astro-ph.SR</span></div>
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                    Space weather at Earth, driven by the solar activity, poses growing risks to satellites around our planet as well as to critical ground-based technological infrastructure. Major space weather contributors are the solar wind and coronal mass ejections whose variable density, speed, temperature, and magnetic field make the automated classification of those structures challenging. In this work, we adapt a foundation model for solar physics, originally trained on Solar Dynamics Observatory imagery, to create embeddings suitable for solar wind structure analysis. These embeddings are concatenated with the spacecraft position and solar magnetic connectivity encoded using Fourier features which generates a neural field-based model. The full deep learning architecture is fine-tuned bridging the gap between remote sensing and in situ observations. Labels are derived from Parker Solar Probe measurements, forming a downstream classification task that maps plasma properties to solar wind structures. Although overall classification performance is modest, likely due to coarse labeling, class imbalance, and limited transferability of the pretrained model, this study demonstrates the feasibility of leveraging foundation model embeddings for in situ solar wind tasks. As a first proof-of-concept, it lays the groundwork for future improvements toward more reliable space weather predictions. The code and configuration files used in this study are publicly available to support reproducibility.
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            <a href="https://www.alphaxiv.org/abs/2511.10547v1" target="_blank" rel="noopener noreferrer">
                通过属性条件人工评估对图像生成中的多样性进行基准测试
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            Benchmarking Diversity in Image Generation via Attribute-Conditional Human Evaluation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Isabela Albuquerque, Ira Ktena, Olivia Wiles, Ivana Kajić, Amal Rannen-Triki, Cr...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于图像生成的多样性评估和基准测试，属于纯粹的计算机视觉领域。虽然提到了评估方法，但这是针对图像生成质量的评估，与推荐系统、搜索或广告中的排序、用户建模、内容理解等核心问题没有直接关联。论文内容不涉及任何推荐、搜索或广告相关的技术应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:48:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10547v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10547v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Despite advances in generation quality, current text-to-image (T2I) models often lack diversity, generating homogeneous outputs. This work introduces a framework to address the need for robust diversity evaluation in T2I models. Our framework systematically assesses diversity by evaluating individual concepts and their relevant factors of variation. Key contributions include: (1) a novel human evaluation template for nuanced diversity assessment; (2) a curated prompt set covering diverse concepts with their identified factors of variation (e.g. prompt: An image of an apple, factor of variation: color); and (3) a methodology for comparing models in terms of human annotations via binomial tests. Furthermore, we rigorously compare various image embeddings for diversity measurement. Notably, our principled approach enables ranking of T2I models by diversity, identifying categories where they particularly struggle. This research offers a robust methodology and insights, paving the way for improvements in T2I model diversity and metric development.
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            <a href="https://www.alphaxiv.org/abs/2511.10518v1" target="_blank" rel="noopener noreferrer">
                SemanticVLA：面向高效机器人操作的语义对齐稀疏化与增强
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            SemanticVLA: Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Li, Renshan Zhang, Rui Shao, Zhijian Fang, Kaiwen Zhou, Zhuotao Tian, Liqian...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器人操作领域，属于具体的机器人应用场景，与推荐系统、搜索或广告的核心技术领域没有直接关联。论文标题中提到的稀疏化和增强技术虽然可能具有通用性，但明确限定在机器人操作应用，无法识别出在推荐系统、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:24:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10518v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10518v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Vision-Language-Action (VLA) models have advanced in robotic manipulation, yet practical deployment remains hindered by two key limitations: 1) perceptual redundancy, where irrelevant visual inputs are processed inefficiently, and 2) superficial instruction-vision alignment, which hampers semantic grounding of actions. In this paper, we propose SemanticVLA, a novel VLA framework that performs Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation. Specifically: 1) To sparsify redundant perception while preserving semantic alignment, Semantic-guided Dual Visual Pruner (SD-Pruner) performs: Instruction-driven Pruner (ID-Pruner) extracts global action cues and local semantic anchors in SigLIP; Spatial-aggregation Pruner (SA-Pruner) compacts geometry-rich features into task-adaptive tokens in DINOv2. 2) To exploit sparsified features and integrate semantics with spatial geometry, Semantic-complementary Hierarchical Fuser (SH-Fuser) fuses dense patches and sparse tokens across SigLIP and DINOv2 for coherent representation. 3) To enhance the transformation from perception to action, Semantic-conditioned Action Coupler (SA-Coupler) replaces the conventional observation-to-DoF approach, yielding more efficient and interpretable behavior modeling for manipulation tasks. Extensive experiments on simulation and real-world tasks show that SemanticVLA sets a new SOTA in both performance and efficiency. SemanticVLA surpasses OpenVLA on LIBERO benchmark by 21.1% in success rate, while reducing training cost and inference latency by 3.0-fold and 2.7-fold.SemanticVLA is open-sourced and publicly available at https://github.com/JiuTian-VL/SemanticVLA
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            <a href="https://www.alphaxiv.org/abs/2511.10500v1" target="_blank" rel="noopener noreferrer">
                用于低剂量CT去噪的带Lambda映射可学习全变分方法
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            Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yusuf Talha Basak, Mehmet Ozan Unal, Metin Ertas, Isa Yildirim
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学影像领域的CT图像去噪技术，属于医疗图像处理范畴。虽然涉及深度学习中的可学习正则化方法，但其应用场景（低剂量CT）和核心问题（医学图像去噪）与推荐系统、搜索或广告领域没有任何直接或间接关联。该技术没有明显的潜力应用于RecSys/Search/Ads领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:05:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10500v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10500v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Although Total Variation (TV) performs well in noise reduction and edge preservation on images, its dependence on the lambda parameter limits its efficiency and makes it difficult to use effectively. In this study, we present a Learnable Total Variation (LTV) framework that couples an unrolled TV solver with a data-driven Lambda Mapping Network (LambdaNet) predicting a per-pixel regularization map. The pipeline is trained end-to-end so that reconstruction and regularization are optimized jointly, yielding spatially adaptive smoothing: strong in homogeneous regions, relaxed near anatomical boundaries. Experiments on the DeepLesion dataset, using a realistic noise model adapted from the LoDoPaB-CT methodology, show consistent gains over classical TV and FBP+U-Net: +2.9 dB PSNR and +6% SSIM on average. LTV provides an interpretable alternative to black-box CNNs and a basis for 3D and data-consistency-driven reconstruction. Our codes are available at: https://github.com/itu-biai/deep_tv_for_ldct
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            <a href="https://www.alphaxiv.org/abs/2511.10394v1" target="_blank" rel="noopener noreferrer">
                LLM-YOLOMS：基于大语言模型的风力涡轮机部件语义解释与故障诊断
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaru Li, Yanxue Wang, Meng Li, Xinming Li, Jianbo Feng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于风力涡轮机部件的故障诊断，属于能源领域的特定应用，与推荐系统、搜索或广告的核心技术无关。虽然使用了LLM技术，但应用场景完全偏离了当前关注的领域，没有任何潜在的RecSys/Search/Ads应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:14:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10394v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10394v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The health condition of wind turbine (WT) components is crucial for ensuring stable and reliable operation. However, existing fault detection methods are largely limited to visual recognition, producing structured outputs that lack semantic interpretability and fail to support maintenance decision-making. To address these limitations, this study proposes an integrated framework that combines YOLOMS with a large language model (LLM) for intelligent fault analysis and diagnosis. Specifically, YOLOMS employs multi-scale detection and sliding-window cropping to enhance fault feature extraction, while a lightweight key-value (KV) mapping module bridges the gap between visual outputs and textual inputs. This module converts YOLOMS detection results into structured textual representations enriched with both qualitative and quantitative attributes. A domain-tuned LLM then performs semantic reasoning to generate interpretable fault analyses and maintenance recommendations. Experiments on real-world datasets demonstrate that the proposed framework achieves a fault detection accuracy of 90.6\% and generates maintenance reports with an average accuracy of 89\%, thereby improving the interpretability of diagnostic results and providing practical decision support for the operation and maintenance of wind turbines.
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            <a href="https://www.alphaxiv.org/abs/2511.10367v1" target="_blank" rel="noopener noreferrer">
                DermAI：通过质量驱动的移动端图像采集实现临床皮肤病学AI分类
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            DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thales Bezerra, Emanoel Thyago, Kelvin Cunha, Rodrigo Abreu, Fábio Papais, Franc...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于皮肤病学的医学AI应用，涉及医学图像分类和移动端数据采集，这属于明确的医学领域应用。根据用户关注的无关主题，医学、生物学等特定领域应用应被排除，且该论文与推荐系统、搜索、广告或相关使能技术没有任何明显关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:48:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10367v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10367v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality checks, and local model adaptation. The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices. In preliminary experiments, models trained on public datasets failed to generalize to our samples, while fine-tuning with local data improved performance. These results highlight the importance of standardized, diverse data collection aligned with healthcare needs and oriented to machine learning development.
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            <a href="https://www.alphaxiv.org/abs/2511.10150v1" target="_blank" rel="noopener noreferrer">
                解耦偏差，对齐分布：面向深度伪造检测的协同公平性优化
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            Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feng Ding, Wenhui Yi, Yunpeng Zhou, Xinan He, Hong Rao, Shu Hu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于深度伪造检测中的公平性问题，这属于隐私、公平性等非技术性主题范畴，已被明确列为无关主题。论文内容与推荐系统、搜索或广告的核心技术进展、LLM技术应用或Transformer架构改进均无直接关联，且未展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 10:04:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10150v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10150v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward different demographic groups, such as gender and race, may lead to systemic misjudgments, exacerbating the digital divide and social inequities. However, current fairness-enhanced detectors often improve fairness at the cost of detection accuracy. To address this challenge, we propose a dual-mechanism collaborative optimization framework. Our proposed method innovatively integrates structural fairness decoupling and global distribution alignment: decoupling channels sensitive to demographic groups at the model architectural level, and subsequently reducing the distance between the overall sample distribution and the distributions corresponding to each demographic group at the feature level. Experimental results demonstrate that, compared with other methods, our framework improves both inter-group and intra-group fairness while maintaining overall detection accuracy across domains.
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            <a href="https://www.alphaxiv.org/abs/2511.10098v1" target="_blank" rel="noopener noreferrer">
                MTAttack：针对大型视觉语言模型的多目标后门攻击
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            MTAttack: Multi-Target Backdoor Attacks against Large Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihan Wang, Guansong Pang, Wenjun Miao, Jin Zheng, Xiao Bai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注大型视觉语言模型的安全漏洞和攻击方法，属于安全领域而非技术进展。虽然涉及视觉语言模型，但焦点是后门攻击这一安全威胁，与推荐系统、搜索、广告的核心技术进展或LLM应用无关。安全主题明确属于被排除的无关话题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:00:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10098v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in Large Visual Language Models (LVLMs) have demonstrated impressive performance across various vision-language tasks by leveraging large-scale image-text pretraining and instruction tuning. However, the security vulnerabilities of LVLMs have become increasingly concerning, particularly their susceptibility to backdoor attacks. Existing backdoor attacks focus on single-target attacks, i.e., targeting a single malicious output associated with a specific trigger. In this work, we uncover multi-target backdoor attacks, where multiple independent triggers corresponding to different attack targets are added in a single pass of training, posing a greater threat to LVLMs in real-world applications. Executing such attacks in LVLMs is challenging since there can be many incorrect trigger-target mappings due to severe feature interference among different triggers. To address this challenge, we propose MTAttack, the first multi-target backdoor attack framework for enforcing accurate multiple trigger-target mappings in LVLMs. The core of MTAttack is a novel optimization method with two constraints, namely Proxy Space Partitioning constraint and Trigger Prototype Anchoring constraint. It jointly optimizes multiple triggers in the latent space, with each trigger independently mapping clean images to a unique proxy class while at the same time guaranteeing their separability. Experiments on popular benchmarks demonstrate a high success rate of MTAttack for multi-target attacks, substantially outperforming existing attack methods. Furthermore, our attack exhibits strong generalizability across datasets and robustness against backdoor defense strategies. These findings highlight the vulnerability of LVLMs to multi-target backdoor attacks and underscore the urgent need for mitigating such threats. Code is available at https://github.com/mala-lab/MTAttack.
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            <a href="https://www.alphaxiv.org/abs/2511.10076v1" target="_blank" rel="noopener noreferrer">
                通过全局旋转扩散和多级约束缓解伴随语音动作生成中的误差累积
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            Mitigating Error Accumulation in Co-Speech Motion Generation via Global Rotation Diffusion and Multi-Level Constraints
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiangyue Zhang, Jianfang Li, Jianqiang Ren, Jiaxu Zhang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于伴随语音的动作生成，这是一个计算机图形学和动画领域的研究方向。虽然提到了扩散模型技术，但其应用场景（语音驱动动作生成）与推荐系统、搜索或广告领域没有直接关联。该技术主要面向虚拟角色动画和数字人交互，无法应用于推荐、搜索或广告中的任何核心任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:30:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10076v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10076v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reliable co-speech motion generation requires precise motion representation and consistent structural priors across all joints. Existing generative methods typically operate on local joint rotations, which are defined hierarchically based on the skeleton structure. This leads to cumulative errors during generation, manifesting as unstable and implausible motions at end-effectors. In this work, we propose GlobalDiff, a diffusion-based framework that operates directly in the space of global joint rotations for the first time, fundamentally decoupling each joint's prediction from upstream dependencies and alleviating hierarchical error accumulation. To compensate for the absence of structural priors in global rotation space, we introduce a multi-level constraint scheme. Specifically, a joint structure constraint introduces virtual anchor points around each joint to better capture fine-grained orientation. A skeleton structure constraint enforces angular consistency across bones to maintain structural integrity. A temporal structure constraint utilizes a multi-scale variational encoder to align the generated motion with ground-truth temporal patterns. These constraints jointly regularize the global diffusion process and reinforce structural awareness. Extensive evaluations on standard co-speech benchmarks show that GlobalDiff generates smooth and accurate motions, improving the performance by 46.0 % compared to the current SOTA under multiple speaker identities.
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            <a href="https://www.alphaxiv.org/abs/2511.10050v1" target="_blank" rel="noopener noreferrer">
                被困于自身光芒：针对交通标志识别系统的可部署与隐蔽性逆反射贴片攻击
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            Trapped by Their Own Light: Deployable and Stealth Retroreflective Patch Attacks on Traffic Sign Recognition Systems
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Go Tsuruoka, Takami Sato, Qi Alfred Chen, Kazuki Nomoto, Ryunosuke Kobayashi, Yu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于交通标志识别系统的对抗性攻击，属于计算机视觉安全领域。虽然涉及识别系统，但主要关注安全漏洞和攻击方法，与推荐系统、搜索、广告的核心技术进展或LLM应用完全无关。该研究属于安全/隐私范畴，明确列在无关主题中。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:48:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10050v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10050v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CV</span></div>
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                    Traffic sign recognition plays a critical role in ensuring safe and efficient transportation of autonomous vehicles but remain vulnerable to adversarial attacks using stickers or laser projections. While existing attack vectors demonstrate security concerns, they suffer from visual detectability or implementation constraints, suggesting unexplored vulnerability surfaces in TSR systems. We introduce the Adversarial Retroreflective Patch (ARP), a novel attack vector that combines the high deployability of patch attacks with the stealthiness of laser projections by utilizing retroreflective materials activated only under victim headlight illumination. We develop a retroreflection simulation method and employ black-box optimization to maximize attack effectiveness. ARP achieves $\geq$93.4\% success rate in dynamic scenarios at 35 meters and $\geq$60\% success rate against commercial TSR systems in real-world conditions. Our user study demonstrates that ARP attacks maintain near-identical stealthiness to benign signs while achieving $\geq$1.9\% higher stealthiness scores than previous patch attacks. We propose the DPR Shield defense, employing strategically placed polarized filters, which achieves $\geq$75\% defense success rates for stop signs and speed limit signs against micro-prism patches.
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            <a href="https://www.alphaxiv.org/abs/2511.10040v1" target="_blank" rel="noopener noreferrer">
                LoG3D：通过局部到全局分区实现超高分辨率3D形状建模
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        <div class="mb-2 text-base text-gray-700">
            LoG3D: Ultra-High-Resolution 3D Shape Modeling via Local-to-Global Partitioning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinran Yang, Shuichang Lai, Jiangjing Lyu, Hongjie Li, Bowen Pan, Yuanqi Li, Jie...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D形状建模技术，属于纯粹的3D视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然提到了局部到全局的分区方法，但这种3D建模技术在当前推荐、搜索或广告系统的实际应用中缺乏明确的适用场景和相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:34:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10040v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10040v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating high-fidelity 3D contents remains a fundamental challenge due to the complexity of representing arbitrary topologies-such as open surfaces and intricate internal structures-while preserving geometric details. Prevailing methods based on signed distance fields (SDFs) are hampered by costly watertight preprocessing and struggle with non-manifold geometries, while point-cloud representations often suffer from sampling artifacts and surface discontinuities. To overcome these limitations, we propose a novel 3D variational autoencoder (VAE) framework built upon unsigned distance fields (UDFs)-a more robust and computationally efficient representation that naturally handles complex and incomplete shapes. Our core innovation is a local-to-global (LoG) architecture that processes the UDF by partitioning it into uniform subvolumes, termed UBlocks. This architecture couples 3D convolutions for capturing local detail with sparse transformers for enforcing global coherence. A Pad-Average strategy further ensures smooth transitions at subvolume boundaries during reconstruction. This modular design enables seamless scaling to ultra-high resolutions up to 2048^3-a regime previously unattainable for 3D VAEs. Experiments demonstrate state-of-the-art performance in both reconstruction accuracy and generative quality, yielding superior surface smoothness and geometric flexibility.
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            <a href="https://www.alphaxiv.org/abs/2511.09999v1" target="_blank" rel="noopener noreferrer">
                MOBA：针对基于LiDAR的3D物体检测系统的材料导向后门攻击
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            MOBA: A Material-Oriented Backdoor Attack against LiDAR-based 3D Object Detection Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Saket S. Chaturvedi, Gaurav Bagwe, Lan Zhang, Pan He, Xiaoyong Yuan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及网络安全领域的后门攻击，属于明确排除的无关主题（安全、隐私）。虽然涉及3D物体检测，但论文焦点是安全攻击而非推荐系统、搜索或广告相关的技术进展，与所有关注领域均无关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:10:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09999v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09999v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    LiDAR-based 3D object detection is widely used in safety-critical systems. However, these systems remain vulnerable to backdoor attacks that embed hidden malicious behaviors during training. A key limitation of existing backdoor attacks is their lack of physical realizability, primarily due to the digital-to-physical domain gap. Digital triggers often fail in real-world settings because they overlook material-dependent LiDAR reflection properties. On the other hand, physically constructed triggers are often unoptimized, leading to low effectiveness or easy detectability.This paper introduces Material-Oriented Backdoor Attack (MOBA), a novel framework that bridges the digital-physical gap by explicitly modeling the material properties of real-world triggers. MOBA tackles two key challenges in physical backdoor design: 1) robustness of the trigger material under diverse environmental conditions, 2) alignment between the physical trigger's behavior and its digital simulation. First, we propose a systematic approach to selecting robust trigger materials, identifying titanium dioxide (TiO_2) for its high diffuse reflectivity and environmental resilience. Second, to ensure the digital trigger accurately mimics the physical behavior of the material-based trigger, we develop a novel simulation pipeline that features: (1) an angle-independent approximation of the Oren-Nayar BRDF model to generate realistic LiDAR intensities, and (2) a distance-aware scaling mechanism to maintain spatial consistency across varying depths. We conduct extensive experiments on state-of-the-art LiDAR-based and Camera-LiDAR fusion models, showing that MOBA achieves a 93.50% attack success rate, outperforming prior methods by over 41%. Our work reveals a new class of physically realizable threats and underscores the urgent need for defenses that account for material-level properties in real-world environments.
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            <a href="https://www.alphaxiv.org/abs/2511.09866v1" target="_blank" rel="noopener noreferrer">
                IPCD：内在点云分解
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            IPCD: Intrinsic Point-Cloud Decomposition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda, Tomoyasu Shimada, Kazuhiko Murasa...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向点云处理，这是计算机视觉和3D视觉领域的核心主题。点云分解与推荐系统、搜索或广告没有任何直接或间接的关联，完全属于被排除的'纯粹视觉'范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 01:57:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09866v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09866v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Point clouds are widely used in various fields, including augmented reality (AR) and robotics, where relighting and texture editing are crucial for realistic visualization. Achieving these tasks requires accurately separating albedo from shade. However, performing this separation on point clouds presents two key challenges: (1) the non-grid structure of point clouds makes conventional image-based decomposition models ineffective, and (2) point-cloud models designed for other tasks do not explicitly consider global-light direction, resulting in inaccurate shade. In this paper, we introduce \textbf{Intrinsic Point-Cloud Decomposition (IPCD)}, which extends image decomposition to the direct decomposition of colored point clouds into albedo and shade. To overcome challenge (1), we propose \textbf{IPCD-Net} that extends image-based model with point-wise feature aggregation for non-grid data processing. For challenge (2), we introduce \textbf{Projection-based Luminance Distribution (PLD)} with a hierarchical feature refinement, capturing global-light ques via multi-view projection. For comprehensive evaluation, we create a synthetic outdoor-scene dataset. Experimental results demonstrate that IPCD-Net reduces cast shadows in albedo and enhances color accuracy in shade. Furthermore, we showcase its applications in texture editing, relighting, and point-cloud registration under varying illumination. Finally, we verify the real-world applicability of IPCD-Net.
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            <a href="https://www.alphaxiv.org/abs/2511.10250v1" target="_blank" rel="noopener noreferrer">
                FineSkiing：一个用于滑雪动作质量评估的细粒度基准
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            FineSkiing: A Fine-grained Benchmark for Skiing Action Quality Assessment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongji Zhang, Siqi Li, Yue Gao, Yu Jiang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于体育动作质量评估的计算机视觉基准，属于纯粹的视觉分析领域。虽然涉及质量评估概念，但与搜索、推荐、广告系统或LLM技术没有任何直接关联，也不涉及Transformer架构改进或异构数据处理。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 12:29:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10250v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10250v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span></div>
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                    Action Quality Assessment (AQA) aims to evaluate and score sports actions, which has attracted widespread interest in recent years. Existing AQA methods primarily predict scores based on features extracted from the entire video, resulting in limited interpretability and reliability. Meanwhile, existing AQA datasets also lack fine-grained annotations for action scores, especially for deduction items and sub-score annotations. In this paper, we construct the first AQA dataset containing fine-grained sub-score and deduction annotations for aerial skiing, which will be released as a new benchmark. For the technical challenges, we propose a novel AQA method, named JudgeMind, which significantly enhances performance and reliability by simulating the judgment and scoring mindset of professional referees. Our method segments the input action video into different stages and scores each stage to enhance accuracy. Then, we propose a stage-aware feature enhancement and fusion module to boost the perception of stage-specific key regions and enhance the robustness to visual changes caused by frequent camera viewpoints switching. In addition, we propose a knowledge-based grade-aware decoder to incorporate possible deduction items as prior knowledge to predict more accurate and reliable scores. Experimental results demonstrate that our method achieves state-of-the-art performance.
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            <a href="https://www.alphaxiv.org/abs/2511.10177v1" target="_blank" rel="noopener noreferrer">
                利用地理空间基础模型进行小型沙质岛屿海岸线划分
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            Utilizing a Geospatial Foundation Model for Coastline Delineation in Small Sandy Islands
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tishya Chhabra, Manisha Bajpai, Walter Zesk, Skylar Tibbits
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于地理空间基础模型在海岸线划分这一特定地理应用，与推荐系统、搜索或广告领域没有任何关联。论文内容属于纯粹的地理信息系统应用，不涉及任何用户行为建模、内容推荐或广告排名等核心技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 10:42:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10177v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10177v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    We present an initial evaluation of NASA and IBM's Prithvi-EO-2.0 geospatial foundation model on shoreline delineation of small sandy islands using satellite images. We curated and labeled a dataset of 225 multispectral images of two Maldivian islands, which we publicly release, and fine-tuned both the 300M and 600M parameter versions of Prithvi on training subsets ranging from 5 to 181 images. Our experiments show that even with as few as 5 training images, the models achieve high performance (F1 of 0.94, IoU of 0.79). Our results demonstrate the strong transfer learning capability of Prithvi, underscoring the potential of such models to support coastal monitoring in data-poor regions.
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            <a href="https://www.alphaxiv.org/abs/2511.10604v1" target="_blank" rel="noopener noreferrer">
                用于哨兵2号土地覆盖制图的多任务全局-局部OBIA-Mamba模型
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            Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zack Dewis, Yimin Zhu, Zhengsen Xu, Mabel Heffring, Saeid Taleghanidoozdoozan, K...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感图像的土地覆盖制图，属于纯粹的计算机视觉应用领域。虽然提到了Mamba架构，但应用场景是地理信息系统和遥感分析，与推荐系统、搜索或广告没有任何直接或潜在的关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:40:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10604v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10604v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification with the following contributions. First, an object-based image analysis (OBIA) Mamba model (OBIA-Mamba) is designed to reduce redundant computation without compromising fine-grained details by using superpixels as Mamba tokens. Second, a global-local (GLocal) dual-branch convolutional neural network (CNN)-mamba architecture is designed to jointly model local spatial detail and global contextual information. Third, a multitask optimization framework is designed to employ dual loss functions to balance local precision with global consistency. The proposed approach is tested on Sentinel-2 imagery in Alberta, Canada, in comparison with several advanced classification approaches, and the results demonstrate that the proposed approach achieves higher classification accuracy and finer details that the other state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.10461v1" target="_blank" rel="noopener noreferrer">
                OpenSR-SRGAN：面向多光谱地球观测数据的灵活超分辨率框架
            </a>
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            OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Simon Donike, Cesar Aybar, Julio Contreras, Luis Gómez-Chova
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感图像的超分辨率技术，属于计算机视觉领域的地球观测应用。虽然涉及生成对抗网络(GAN)技术，但其应用场景（多光谱地球观测数据）与推荐系统、搜索或广告领域没有直接关联，也不符合异构数据统一建模或Transformer架构进展等关注重点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:28:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10461v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10461v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    We present OpenSR-SRGAN, an open and modular framework for single-image super-resolution in Earth Observation. The software provides a unified implementation of SRGAN-style models that is easy to configure, extend, and apply to multispectral satellite data such as Sentinel-2. Instead of requiring users to modify model code, OpenSR-SRGAN exposes generators, discriminators, loss functions, and training schedules through concise configuration files, making it straightforward to switch between architectures, scale factors, and band setups. The framework is designed as a practical tool and benchmark implementation rather than a state-of-the-art model. It ships with ready-to-use configurations for common remote sensing scenarios, sensible default settings for adversarial training, and built-in hooks for logging, validation, and large-scene inference. By turning GAN-based super-resolution into a configuration-driven workflow, OpenSR-SRGAN lowers the entry barrier for researchers and practitioners who wish to experiment with SRGANs, compare models in a reproducible way, and deploy super-resolution pipelines across diverse Earth-observation datasets.
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            <a href="https://www.alphaxiv.org/abs/2511.10431v1" target="_blank" rel="noopener noreferrer">
                RodEpil：用于癫痫检测和基准评估的实验室啮齿动物视频数据集
            </a>
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            RodEpil: A Video Dataset of Laboratory Rodents for Seizure Detection and Benchmark Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniele Perlo, Vladimir Despotovic, Selma Boudissa, Sang-Yoon Kim, Petr Nazarov,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的癫痫检测数据集，涉及实验室啮齿动物视频分析，这与推荐系统、搜索或广告领域完全无关。论文内容属于医学/生物学应用范畴，明确属于用户指定的不相关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:53:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10431v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10431v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce a curated video dataset of laboratory rodents for automatic detection of convulsive events. The dataset contains short (10~s) top-down and side-view video clips of individual rodents, labeled at clip level as normal activity or seizure. It includes 10,101 negative samples and 2,952 positive samples collected from 19 subjects. We describe the data curation, annotation protocol and preprocessing pipeline, and report baseline experiments using a transformer-based video classifier (TimeSformer). Experiments employ five-fold cross-validation with strict subject-wise partitioning to prevent data leakage (no subject appears in more than one fold). Results show that the TimeSformer architecture enables discrimination between seizure and normal activity with an average F1-score of 97%. The dataset and baseline code are publicly released to support reproducible research on non-invasive, video-based monitoring in preclinical epilepsy research. RodEpil Dataset access - DOI: 10.5281/zenodo.17601357
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            <a href="https://www.alphaxiv.org/abs/2511.10387v1" target="_blank" rel="noopener noreferrer">
                基于物理信息的Transformer-VAE用于生物物理参数估计：Sentinel-2影像中的PROSAIL模型反演
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            Physics informed Transformer-VAE for biophysical parameter estimation: PROSAIL model inversion in Sentinel-2 imagery
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Prince Mensah, Pelumi Victor Aderinto, Ibrahim Salihu Yusuf, Arnu Pretorius
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感领域的生物物理参数估计和物理模型反演，属于地球科学和遥感应用范畴。虽然使用了Transformer架构，但其应用场景（Sentinel-2卫星影像、PROSAIL植被模型）与推荐系统、搜索或广告领域完全无关。论文的技术方法（物理信息神经网络）也没有明显的跨领域应用潜力到RecSys/Search/Ads中。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:08:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10387v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10387v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate retrieval of vegetation biophysical variables from satellite imagery is crucial for ecosystem monitoring and agricultural management. In this work, we propose a physics-informed Transformer-VAE architecture to invert the PROSAIL radiative transfer model for simultaneous estimation of key canopy parameters from Sentinel-2 data. Unlike previous hybrid approaches that require real satellite images for self-supevised training. Our model is trained exclusively on simulated data, yet achieves performance on par with state-of-the-art methods that utilize real imagery. The Transformer-VAE incorporates the PROSAIL model as a differentiable physical decoder, ensuring that inferred latent variables correspond to physically plausible leaf and canopy properties. We demonstrate retrieval of leaf area index (LAI) and canopy chlorophyll content (CCC) on real-world field datasets (FRM4Veg and BelSAR) with accuracy comparable to models trained with real Sentinel-2 data. Our method requires no in-situ labels or calibration on real images, offering a cost-effective and self-supervised solution for global vegetation monitoring. The proposed approach illustrates how integrating physical models with advanced deep networks can improve the inversion of RTMs, opening new prospects for large-scale, physically-constrained remote sensing of vegetation traits.
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            <a href="https://www.alphaxiv.org/abs/2511.10068v1" target="_blank" rel="noopener noreferrer">
                感知、行动与修正：置信度不足以支撑高光谱分类
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            Perceive, Act and Correct: Confidence Is Not Enough for Hyperspectral Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muzhou Yang, Wuzhou Quan, Mingqiang Wei
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于高光谱图像分类，这是一个纯粹的计算机视觉领域，与推荐系统、搜索或广告没有直接关联。标题中提到的置信度概念虽然通用，但论文的应用场景（高光谱分类）属于遥感或地球观测领域，完全超出了当前关注的范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:14:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10068v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10068v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Confidence alone is often misleading in hyperspectral image classification, as models tend to mistake high predictive scores for correctness while lacking awareness of uncertainty. This leads to confirmation bias, especially under sparse annotations or class imbalance, where models overfit confident errors and fail to generalize. We propose CABIN (Cognitive-Aware Behavior-Informed learNing), a semi-supervised framework that addresses this limitation through a closed-loop learning process of perception, action, and correction. CABIN first develops perceptual awareness by estimating epistemic uncertainty, identifying ambiguous regions where errors are likely to occur. It then acts by adopting an Uncertainty-Guided Dual Sampling Strategy, selecting uncertain samples for exploration while anchoring confident ones as stable pseudo-labels to reduce bias. To correct noisy supervision, CABIN introduces a Fine-Grained Dynamic Assignment Strategy that categorizes pseudo-labeled data into reliable, ambiguous, and noisy subsets, applying tailored losses to enhance generalization. Experimental results show that a wide range of state-of-the-art methods benefit from the integration of CABIN, with improved labeling efficiency and performance.
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            <a href="https://www.alphaxiv.org/abs/2511.10060v1" target="_blank" rel="noopener noreferrer">
                用于医疗行为评估的多变量高斯表示学习
            </a>
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            Multivariate Gaussian Representation Learning for Medical Action Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luming Yang, Haoxian Liu, Siqing Li, Alper Yilmaz
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于医疗领域的应用（医疗行为评估），这属于明确排除的无关主题范畴。虽然表示学习技术本身可能具有通用性，但论文的特定医疗应用使其与推荐系统、搜索或广告领域完全不相关。没有证据表明该技术有潜力应用于我的关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 08:01:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10060v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10060v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Fine-grained action evaluation in medical vision faces unique challenges due to the unavailability of comprehensive datasets, stringent precision requirements, and insufficient spatiotemporal dynamic modeling of very rapid actions. To support development and evaluation, we introduce CPREval-6k, a multi-view, multi-label medical action benchmark containing 6,372 expert-annotated videos with 22 clinical labels. Using this dataset, we present GaussMedAct, a multivariate Gaussian encoding framework, to advance medical motion analysis through adaptive spatiotemporal representation learning. Multivariate Gaussian Representation projects the joint motions to a temporally scaled multi-dimensional space, and decomposes actions into adaptive 3D Gaussians that serve as tokens. These tokens preserve motion semantics through anisotropic covariance modeling while maintaining robustness to spatiotemporal noise. Hybrid Spatial Encoding, employing a Cartesian and Vector dual-stream strategy, effectively utilizes skeletal information in the form of joint and bone features. The proposed method achieves 92.1% Top-1 accuracy with real-time inference on the benchmark, outperforming the ST-GCN baseline by +5.9% accuracy with only 10% FLOPs. Cross-dataset experiments confirm the superiority of our method in robustness.
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            <a href="https://www.alphaxiv.org/abs/2511.10013v1" target="_blank" rel="noopener noreferrer">
                MIRNet：将基于约束图推理与预训练相结合用于诊断医学影像
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            MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shufeng Kong, Zijie Wang, Nuan Cui, Hao Tang, Yihan Meng, Yuanyuan Wei, Feifan C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于诊断医学影像这一明确的无关领域，属于医疗应用范畴。虽然提到了图推理和预训练技术，但其医疗影像的特定应用场景与推荐系统、搜索或广告领域没有任何直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:30:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10013v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10013v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels--representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.10370v1" target="_blank" rel="noopener noreferrer">
                SHRUG-FM：面向地球观测的可靠性感知基础模型
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            SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kai-Hendrik Cohrs, Zuzanna Osika, Maria Gonzalez-Calabuig, Vishal Nedungadi, Rub...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于地球观测领域的基础模型，这是一个与遥感、地理信息系统相关的特定领域应用。虽然提到了基础模型技术，但其应用场景完全限定在地球观测这一专业领域，与推荐系统、搜索或广告没有任何直接或潜在的关联。该论文的技术内容无法迁移到我的关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:48:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10370v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10370v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Geospatial foundation models for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for reliability-aware prediction that integrates three complementary signals: out-of-distribution (OOD) detection in the input space, OOD detection in the embedding space and task-specific predictive uncertainty. Applied to burn scar segmentation, SHRUG-FM shows that OOD scores correlate with lower performance in specific environmental conditions, while uncertainty-based flags help discard many poorly performing predictions. Linking these flags to land cover attributes from HydroATLAS shows that failures are not random but concentrated in certain geographies, such as low-elevation zones and large river areas, likely due to underrepresentation in pretraining data. SHRUG-FM provides a pathway toward safer and more interpretable deployment of GFMs in climate-sensitive applications, helping bridge the gap between benchmark performance and real-world reliability.
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            <a href="https://www.alphaxiv.org/abs/2511.10209v1" target="_blank" rel="noopener noreferrer">
                LiNeXt：基于高效非扩散架构重新审视激光雷达补全
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            LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenzhe He, Xiaojun Chen, Ruiqi Wang, Ruihui Li, Huilong Pi, Jiapeng Zhang, Zhuo ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于激光雷达补全技术，属于纯粹的3D视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。激光雷达数据处理在自动驾驶和机器人领域有应用，但缺乏在RecSys/Search/Ads中的潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 11:28:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10209v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10209v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models for high-fidelity reconstruction. However, their multi-step iterative sampling incurs significant computational overhead, limiting its real-time applicability. To address this, we propose LiNeXt-a lightweight, non-diffusion network optimized for rapid and accurate point cloud completion. Specifically, LiNeXt first applies the Noise-to-Coarse (N2C) Module to denoise the input noisy point cloud in a single pass, thereby obviating the multi-step iterative sampling of diffusion-based methods. The Refine Module then takes the coarse point cloud and its intermediate features from the N2C Module to perform more precise refinement, further enhancing structural completeness. Furthermore, we observe that LiDAR point clouds exhibit a distance-dependent spatial distribution, being densely sampled at proximal ranges and sparsely sampled at distal ranges. Accordingly, we propose the Distance-aware Selected Repeat strategy to generate a more uniformly distributed noisy point cloud. On the SemanticKITTI dataset, LiNeXt achieves a 199.8x speedup in inference, reduces Chamfer Distance by 50.7%, and uses only 6.1% of the parameters compared with LiDiff. These results demonstrate the superior efficiency and effectiveness of LiNeXt for real-time scene completion.
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            <a href="https://www.alphaxiv.org/abs/2511.09827v1" target="_blank" rel="noopener noreferrer">
                AHA！使用高斯泼溅在多样化场景中动画化人类化身
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            AHA! Animating Human Avatars in Diverse Scenes with Gaussian Splatting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aymen Mir, Jian Wang, Riza Alp Guler, Chuan Guo, Gerard Pons-Moll, Bing Zhou
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的人类化身动画和3D场景渲染技术，使用高斯泼溅方法。这与推荐系统、搜索或广告的核心技术焦点完全无关，也不涉及LLM、Transformer架构或异构数据建模。该技术属于纯粹的视觉领域，没有明确的推荐/搜索/广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 00:19:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09827v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09827v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present a novel framework for animating humans in 3D scenes using 3D Gaussian Splatting (3DGS), a neural scene representation that has recently achieved state-of-the-art photorealistic results for novel-view synthesis but remains under-explored for human-scene animation and interaction. Unlike existing animation pipelines that use meshes or point clouds as the underlying 3D representation, our approach introduces the use of 3DGS as the 3D representation to the problem of animating humans in scenes. By representing humans and scenes as Gaussians, our approach allows for geometry-consistent free-viewpoint rendering of humans interacting with 3D scenes. Our key insight is that the rendering can be decoupled from the motion synthesis and each sub-problem can be addressed independently, without the need for paired human-scene data. Central to our method is a Gaussian-aligned motion module that synthesizes motion without explicit scene geometry, using opacity-based cues and projected Gaussian structures to guide human placement and pose alignment. To ensure natural interactions, we further propose a human-scene Gaussian refinement optimization that enforces realistic contact and navigation. We evaluate our approach on scenes from Scannet++ and the SuperSplat library, and on avatars reconstructed from sparse and dense multi-view human capture. Finally, we demonstrate that our framework allows for novel applications such as geometry-consistent free-viewpoint rendering of edited monocular RGB videos with new animated humans, showcasing the unique advantage of 3DGS for monocular video-based human animation.
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            <a href="https://www.alphaxiv.org/abs/2511.10352v1" target="_blank" rel="noopener noreferrer">
                FOUND：基于傅里叶的冯·米塞斯分布用于目标检测中的鲁棒单域泛化
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            FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mengzhu Wang, Changyuan Deng, Shanshan Wang, Nan Yin, Long Lan, Liang Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的目标检测和域泛化问题，与推荐系统、搜索或广告的核心技术没有直接关联。虽然提到了分布建模和泛化，但这些概念在视觉检测中的具体应用无法直接转化为推荐/搜索场景中的异构数据处理或序列建模需求。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 14:28:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10352v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10352v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Single Domain Generalization (SDG) for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains. While recent methods like CLIP-based semantic augmentation have shown promise, they often overlook the underlying structure of feature distributions and frequency-domain characteristics that are critical for robustness. In this paper, we propose a novel framework that enhances SDG object detection by integrating the von Mises-Fisher (vMF) distribution and Fourier transformation into a CLIP-guided pipeline. Specifically, we model the directional features of object representations using vMF to better capture domain-invariant semantic structures in the embedding space. Additionally, we introduce a Fourier-based augmentation strategy that perturbs amplitude and phase components to simulate domain shifts in the frequency domain, further improving feature robustness. Our method not only preserves the semantic alignment benefits of CLIP but also enriches feature diversity and structural consistency across domains. Extensive experiments on the diverse weather-driving benchmark demonstrate that our approach outperforms the existing state-of-the-art method.
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            <a href="https://www.alphaxiv.org/abs/2511.10308v1" target="_blank" rel="noopener noreferrer">
                重新评估深度神经网络在行人检测中的应用
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            Revisiting Evaluation of Deep Neural Networks for Pedestrian Detection
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Patrick Feifel, Benedikt Franke, Frank Bonarens, Frank Köster, Arne Raulf, Fried...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的行人检测任务，属于纯粹的视觉应用领域。虽然涉及深度神经网络，但没有任何明确的连接或潜在应用指向推荐系统、搜索或广告领域。该研究完全专注于视觉检测问题，与当前关注的核心领域无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 13:45:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10308v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10308v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Reliable pedestrian detection represents a crucial step towards automated driving systems. However, the current performance benchmarks exhibit weaknesses. The currently applied metrics for various subsets of a validation dataset prohibit a realistic performance evaluation of a DNN for pedestrian detection. As image segmentation supplies fine-grained information about a street scene, it can serve as a starting point to automatically distinguish between different types of errors during the evaluation of a pedestrian detector. In this work, eight different error categories for pedestrian detection are proposed and new metrics are proposed for performance comparison along these error categories. We use the new metrics to compare various backbones for a simplified version of the APD, and show a more fine-grained and robust way to compare models with each other especially in terms of safety-critical performance. We achieve SOTA on CityPersons-reasonable (without extra training data) by using a rather simple architecture.
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            <a href="https://www.alphaxiv.org/abs/2511.10539v1" target="_blank" rel="noopener noreferrer">
                基于人本上下文的动态虚拟形象-场景渲染
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            Dynamic Avatar-Scene Rendering from Human-centric Context
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenqing Wang, Haosen Yang, Josef Kittler, Xiatian Zhu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的虚拟形象和场景渲染技术，属于纯粹的视觉计算领域。标题中提到的'人本上下文'指的是视觉和空间上下文，而非推荐系统或搜索中所需的用户行为、兴趣或意图上下文。该技术没有明显的潜在应用场景能够直接服务于推荐系统、搜索或广告的排名与匹配核心任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:39:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10539v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10539v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reconstructing dynamic humans interacting with real-world environments from monocular videos is an important and challenging task. Despite considerable progress in 4D neural rendering, existing approaches either model dynamic scenes holistically or model scenes and backgrounds separately aim to introduce parametric human priors. However, these approaches either neglect distinct motion characteristics of various components in scene especially human, leading to incomplete reconstructions, or ignore the information exchange between the separately modeled components, resulting in spatial inconsistencies and visual artifacts at human-scene boundaries. To address this, we propose {\bf Separate-then-Map} (StM) strategy that introduces a dedicated information mapping mechanism to bridge separately defined and optimized models. Our method employs a shared transformation function for each Gaussian attribute to unify separately modeled components, enhancing computational efficiency by avoiding exhaustive pairwise interactions while ensuring spatial and visual coherence between humans and their surroundings. Extensive experiments on monocular video datasets demonstrate that StM significantly outperforms existing state-of-the-art methods in both visual quality and rendering accuracy, particularly at challenging human-scene interaction boundaries.
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            <a href="https://www.alphaxiv.org/abs/2511.10432v1" target="_blank" rel="noopener noreferrer">
                基于组织学信息的全组织切片分块方法可提升癌症复发与基因突变预测的可解释性与预测性能
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            Histology-informed tiling of whole tissue sections improves the interpretability and predictability of cancer relapse and genetic alterations
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Willem Bonnaffé, Yang Hu, Andrea Chatrian, Mengran Fan, Stefano Malacrino, Sandy...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域的癌症组织学分析，涉及组织切片、癌症复发预测和基因突变检测等生物医学主题。这些内容完全属于被排除的'Medical, Biology'领域，与推荐系统、搜索、广告或相关LLM技术没有任何关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:54:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10432v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10432v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">q-bio.QM</span><span class="category-tag">q-bio.TO</span></div>
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                    Histopathologists establish cancer grade by assessing histological structures, such as glands in prostate cancer. Yet, digital pathology pipelines often rely on grid-based tiling that ignores tissue architecture. This introduces irrelevant information and limits interpretability. We introduce histology-informed tiling (HIT), which uses semantic segmentation to extract glands from whole slide images (WSIs) as biologically meaningful input patches for multiple-instance learning (MIL) and phenotyping. Trained on 137 samples from the ProMPT cohort, HIT achieved a gland-level Dice score of 0.83 +/- 0.17. By extracting 380,000 glands from 760 WSIs across ICGC-C and TCGA-PRAD cohorts, HIT improved MIL models AUCs by 10% for detecting copy number variation (CNVs) in genes related to epithelial-mesenchymal transitions (EMT) and MYC, and revealed 15 gland clusters, several of which were associated with cancer relapse, oncogenic mutations, and high Gleason. Therefore, HIT improved the accuracy and interpretability of MIL predictions, while streamlining computations by focussing on biologically meaningful structures during feature extraction.
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            <a href="https://www.alphaxiv.org/abs/2511.10391v1" target="_blank" rel="noopener noreferrer">
                GrounDiff：基于扩散模型的数字表面模型地面表面生成
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            GrounDiff: Diffusion-Based Ground Surface Generation from Digital Surface Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Oussema Dhaouadi, Johannes Meier, Jacques Kaiser, Daniel Cremers
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的扩散模型应用，用于从数字表面模型生成地面表面。这与推荐系统、搜索或广告的核心领域进展、LLM技术或Transformer架构改进无关，也不涉及处理异构数据的统一建模方法。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:12:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10391v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10391v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Digital Terrain Models (DTMs) represent the bare-earth elevation and are important in numerous geospatial applications. Such data models cannot be directly measured by sensors and are typically generated from Digital Surface Models (DSMs) derived from LiDAR or photogrammetry. Traditional filtering approaches rely on manually tuned parameters, while learning-based methods require well-designed architectures, often combined with post-processing. To address these challenges, we introduce Ground Diffusion (GrounDiff), the first diffusion-based framework that iteratively removes non-ground structures by formulating the problem as a denoising task. We incorporate a gated design with confidence-guided generation that enables selective filtering. To increase scalability, we further propose Prior-Guided Stitching (PrioStitch), which employs a downsampled global prior automatically generated using GrounDiff to guide local high-resolution predictions. We evaluate our method on the DSM-to-DTM translation task across diverse datasets, showing that GrounDiff consistently outperforms deep learning-based state-of-the-art methods, reducing RMSE by up to 93% on ALS2DTM and up to 47% on USGS benchmarks. In the task of road reconstruction, which requires both high precision and smoothness, our method achieves up to 81% lower distance error compared to specialized techniques on the GeRoD benchmark, while maintaining competitive surface smoothness using only DSM inputs, without task-specific optimization. Our variant for road reconstruction, GrounDiff+, is specifically designed to produce even smoother surfaces, further surpassing state-of-the-art methods. The project page is available at https://deepscenario.github.io/GrounDiff/.
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            <a href="https://www.alphaxiv.org/abs/2511.10385v1" target="_blank" rel="noopener noreferrer">
                SAMIRO：基于预训练模型作为先验的空间注意力互信息正则化用于车道线检测
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            SAMIRO: Spatial Attention Mutual Information Regularization with a Pre-trained Model as Oracle for Lane Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hyunjong Lee, Jangho Lee, Jaekoo Lee
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的车道线检测任务，属于纯粹的视觉应用领域。虽然涉及注意力机制和预训练模型，但其应用场景（自动驾驶中的车道检测）与推荐系统、搜索或广告领域没有任何直接关联，也不具备将这些技术迁移到这些领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:01:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10385v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10385v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Lane detection is an important topic in the future mobility solutions. Real-world environmental challenges such as background clutter, varying illumination, and occlusions pose significant obstacles to effective lane detection, particularly when relying on data-driven approaches that require substantial effort and cost for data collection and annotation. To address these issues, lane detection methods must leverage contextual and global information from surrounding lanes and objects. In this paper, we propose a Spatial Attention Mutual Information Regularization with a pre-trained model as an Oracle, called SAMIRO. SAMIRO enhances lane detection performance by transferring knowledge from a pretrained model while preserving domain-agnostic spatial information. Leveraging SAMIRO's plug-and-play characteristic, we integrate it into various state-of-the-art lane detection approaches and conduct extensive experiments on major benchmarks such as CULane, Tusimple, and LLAMAS. The results demonstrate that SAMIRO consistently improves performance across different models and datasets. The code will be made available upon publication.
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            <a href="https://www.alphaxiv.org/abs/2511.10023v1" target="_blank" rel="noopener noreferrer">
                基于定制化CNN模型的早产儿视网膜病变高效自动诊断
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            Efficient Automated Diagnosis of Retinopathy of Prematurity by Customize CNN Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Farzan Saeedi, Sanaz Keshvari, Nasser Shoeibi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的视网膜病变诊断，使用CNN模型进行医疗图像分析。这属于明确的医学应用领域，与推荐系统、搜索或广告技术完全无关。论文内容涉及医疗诊断这一被明确排除的领域，没有任何潜在的应用于推荐系统、搜索或广告的可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:00:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10023v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10023v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    This paper encompasses an in-depth examination of Retinopathy of Prematurity (ROP) diagnosis, employing advanced deep learning methodologies. Our focus centers on refining and evaluating CNN-based approaches for precise and efficient ROP detection. We navigate the complexities of dataset curation, preprocessing strategies, and model architecture, aligning with research objectives encompassing model effectiveness, computational cost analysis, and time complexity assessment. Results underscore the supremacy of tailored CNN models over pre-trained counterparts, evident in heightened accuracy and F1-scores. Implementation of a voting system further enhances performance. Additionally, our study reveals the potential of the proposed customized CNN model to alleviate computational burdens associated with deep neural networks. Furthermore, we showcase the feasibility of deploying these models within dedicated software and hardware configurations, highlighting their utility as valuable diagnostic aids in clinical settings. In summary, our discourse significantly contributes to ROP diagnosis, unveiling the efficacy of deep learning models in enhancing diagnostic precision and efficiency.
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            <a href="https://www.alphaxiv.org/abs/2511.10003v1" target="_blank" rel="noopener noreferrer">
                DBGroup：用于弱监督3D实例分割的双分支点分组方法
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            DBGroup: Dual-Branch Point Grouping for Weakly Supervised 3D Instance Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuexun Liu, Xiaoxu Xu, Qiudan Zhang, Lin Ma, Xu Wang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D计算机视觉中的实例分割任务，属于纯粹的3D视觉研究领域。论文内容涉及点云处理、实例分割和弱监督学习，与搜索、推荐、广告系统没有任何直接或间接的关联，也不涉及LLM技术或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 06:12:13
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                <a href="https://arxiv.org/abs/2511.10003v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10003v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Weakly supervised 3D instance segmentation is essential for 3D scene understanding, especially as the growing scale of data and high annotation costs associated with fully supervised approaches. Existing methods primarily rely on two forms of weak supervision: one-thing-one-click annotations and bounding box annotations, both of which aim to reduce labeling efforts. However, these approaches still encounter limitations, including labor-intensive annotation processes, high complexity, and reliance on expert annotators. To address these challenges, we propose \textbf{DBGroup}, a two-stage weakly supervised 3D instance segmentation framework that leverages scene-level annotations as a more efficient and scalable alternative. In the first stage, we introduce a Dual-Branch Point Grouping module to generate pseudo labels guided by semantic and mask cues extracted from multi-view images. To further improve label quality, we develop two refinement strategies: Granularity-Aware Instance Merging and Semantic Selection and Propagation. The second stage involves multi-round self-training on an end-to-end instance segmentation network using the refined pseudo-labels. Additionally, we introduce an Instance Mask Filter strategy to address inconsistencies within the pseudo labels. Extensive experiments demonstrate that DBGroup achieves competitive performance compared to sparse-point-level supervised 3D instance segmentation methods, while surpassing state-of-the-art scene-level supervised 3D semantic segmentation approaches. Code is available at https://github.com/liuxuexun/DBGroup.
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            <a href="https://www.alphaxiv.org/abs/2511.09944v1" target="_blank" rel="noopener noreferrer">
                TSPE-GS：基于3D高斯泼溅的半透明表面重建概率深度提取方法
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            TSPE-GS: Probabilistic Depth Extraction for Semi-Transparent Surface Reconstruction via 3D Gaussian Splatting
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiyuan Xu, Nan Min, Yuhang Guo, Tong Wei
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉中的半透明表面重建和深度提取技术，属于纯粹的计算机视觉领域。虽然使用了高斯泼溅方法，但该技术主要应用于3D重建和图形学，与推荐系统、搜索或广告的排名和建模需求没有直接关联。论文内容不涉及用户行为建模、内容理解或个性化排序等RecSys/Search/Ads核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:18:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09944v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09944v1
                </a>
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                    3D Gaussian Splatting offers a strong speed-quality trade-off but struggles to reconstruct semi-transparent surfaces because most methods assume a single depth per pixel, which fails when multiple surfaces are visible. We propose TSPE-GS (Transparent Surface Probabilistic Extraction for Gaussian Splatting), which uniformly samples transmittance to model a pixel-wise multi-modal distribution of opacity and depth, replacing the prior single-peak assumption and resolving cross-surface depth ambiguity. By progressively fusing truncated signed distance functions, TSPE-GS reconstructs external and internal surfaces separately within a unified framework. The method generalizes to other Gaussian-based reconstruction pipelines without extra training overhead. Extensive experiments on public and self-collected semi-transparent and opaque datasets show TSPE-GS significantly improves semi-transparent geometry reconstruction while maintaining performance on opaque scenes.
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            <a href="https://www.alphaxiv.org/abs/2511.09894v1" target="_blank" rel="noopener noreferrer">
                EgoEMS：用于紧急医疗服务认知辅助的高保真多模态自我中心数据集
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            EgoEMS: A High-Fidelity Multimodal Egocentric Dataset for Cognitive Assistance in Emergency Medical Services
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Keshara Weerasinghe, Xueren Ge, Tessa Heick, Lahiru Nuwan Wijayasingha, Anthony ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学领域（紧急医疗服务）的数据集构建，这属于明确的无关主题范畴。虽然涉及多模态数据，但其应用场景完全限定在医疗认知辅助，与推荐系统、搜索或广告领域没有任何技术关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 02:55:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09894v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09894v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Emergency Medical Services (EMS) are critical to patient survival in emergencies, but first responders often face intense cognitive demands in high-stakes situations. AI cognitive assistants, acting as virtual partners, have the potential to ease this burden by supporting real-time data collection and decision making. In pursuit of this vision, we introduce EgoEMS, the first end-to-end, high-fidelity, multimodal, multiperson dataset capturing over 20 hours of realistic, procedural EMS activities from an egocentric view in 233 simulated emergency scenarios performed by 62 participants, including 46 EMS professionals. Developed in collaboration with EMS experts and aligned with national standards, EgoEMS is captured using an open-source, low-cost, and replicable data collection system and is annotated with keysteps, timestamped audio transcripts with speaker diarization, action quality metrics, and bounding boxes with segmentation masks. Emphasizing realism, the dataset includes responder-patient interactions reflecting real-world emergency dynamics. We also present a suite of benchmarks for real-time multimodal keystep recognition and action quality estimation, essential for developing AI support tools for EMS. We hope EgoEMS inspires the research community to push the boundaries of intelligent EMS systems and ultimately contribute to improved patient outcomes.
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            <a href="https://www.alphaxiv.org/abs/2511.09834v1" target="_blank" rel="noopener noreferrer">
                CertMask：通过理论最优掩码覆盖实现可验证的对抗性补丁防御
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            CertMask: Certifiable Defense Against Adversarial Patches via Theoretically Optimal Mask Coverage
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuntao Lyu, Ching-Chi Lin, Abdullah Al Arafat, Georg von der Brüggen, Jian-Jia C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于对抗性补丁防御和可验证安全性，属于计算机安全领域。虽然对抗性攻击可能影响推荐/搜索系统中的视觉内容，但论文的核心技术（可验证防御、补丁检测）与我的核心关注点（推荐系统算法、LLM技术、Transformer架构、多模态建模）没有直接关联。这属于明确排除的安全/隐私相关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 00:33:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.09834v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09834v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Adversarial patch attacks inject localized perturbations into images to mislead deep vision models. These attacks can be physically deployed, posing serious risks to real-world applications. In this paper, we propose CertMask, a certifiably robust defense that constructs a provably sufficient set of binary masks to neutralize patch effects with strong theoretical guarantees. While the state-of-the-art approach (PatchCleanser) requires two rounds of masking and incurs $O(n^2)$ inference cost, CertMask performs only a single round of masking with $O(n)$ time complexity, where $n$ is the cardinality of the mask set to cover an input image. Our proposed mask set is computed using a mathematically rigorous coverage strategy that ensures each possible patch location is covered at least $k$ times, providing both efficiency and robustness. We offer a theoretical analysis of the coverage condition and prove its sufficiency for certification. Experiments on ImageNet, ImageNette, and CIFAR-10 show that CertMask improves certified robust accuracy by up to +13.4\% over PatchCleanser, while maintaining clean accuracy nearly identical to the vanilla model.
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            <a href="https://www.alphaxiv.org/abs/2511.10597v1" target="_blank" rel="noopener noreferrer">
                从2D到3D无需额外负担：数字乳腺断层合成中数据高效的癌症检测
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            From 2D to 3D Without Extra Baggage: Data-Efficient Cancer Detection in Digital Breast Tomosynthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yen Nhi Truong Vu, Dan Guo, Sripad Joshi, Harshit Kumar, Jason Su, Thomas Paul M...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的癌症检测应用，具体针对数字乳腺断层合成技术。这属于明确的医学/生物学应用领域，完全落在用户指定的无关主题范围内。论文内容与推荐系统、搜索、广告或相关使能技术没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 18:35:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10597v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10597v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Digital Breast Tomosynthesis (DBT) enhances finding visibility for breast cancer detection by providing volumetric information that reduces the impact of overlapping tissues; however, limited annotated data has constrained the development of deep learning models for DBT. To address data scarcity, existing methods attempt to reuse 2D full-field digital mammography (FFDM) models by either flattening DBT volumes or processing slices individually, thus discarding volumetric information. Alternatively, 3D reasoning approaches introduce complex architectures that require more DBT training data. Tackling these drawbacks, we propose M&M-3D, an architecture that enables learnable 3D reasoning while remaining parameter-free relative to its FFDM counterpart, M&M. M&M-3D constructs malignancy-guided 3D features, and 3D reasoning is learned through repeatedly mixing these 3D features with slice-level information. This is achieved by modifying operations in M&M without adding parameters, thus enabling direct weight transfer from FFDM. Extensive experiments show that M&M-3D surpasses 2D projection and 3D slice-based methods by 11-54% for localization and 3-10% for classification. Additionally, M&M-3D outperforms complex 3D reasoning variants by 20-47% for localization and 2-10% for classification in the low-data regime, while matching their performance in high-data regime. On the popular BCS-DBT benchmark, M&M-3D outperforms previous top baseline by 4% for classification and 10% for localization.
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            <a href="https://www.alphaxiv.org/abs/2511.10555v1" target="_blank" rel="noopener noreferrer">
                一种风格对应一个代码：通过离散风格空间解锁代码到风格的图像生成
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            A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huijie Liu, Shuhao Cui, Haoxiang Cao, Shuai Ma, Kai Wu, Guoliang Kang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于代码到图像的生成和风格控制，属于纯粹的图像生成领域。虽然涉及离散表示学习，但论文内容与推荐系统、搜索或广告的核心技术无关，也不涉及LLM在推荐/搜索/广告中的潜在应用。这属于AIGC和内容生成范畴，明确在无关主题列表中。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 17:56:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10555v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10555v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Innovative visual stylization is a cornerstone of artistic creation, yet generating novel and consistent visual styles remains a significant challenge. Existing generative approaches typically rely on lengthy textual prompts, reference images, or parameter-efficient fine-tuning to guide style-aware image generation, but often struggle with style consistency, limited creativity, and complex style representations. In this paper, we affirm that a style is worth one numerical code by introducing the novel task, code-to-style image generation, which produces images with novel, consistent visual styles conditioned solely on a numerical style code. To date, this field has only been primarily explored by the industry (e.g., Midjourney), with no open-source research from the academic community. To fill this gap, we propose CoTyle, the first open-source method for this task. Specifically, we first train a discrete style codebook from a collection of images to extract style embeddings. These embeddings serve as conditions for a text-to-image diffusion model (T2I-DM) to generate stylistic images. Subsequently, we train an autoregressive style generator on the discrete style embeddings to model their distribution, allowing the synthesis of novel style embeddings. During inference, a numerical style code is mapped to a unique style embedding by the style generator, and this embedding guides the T2I-DM to generate images in the corresponding style. Unlike existing methods, our method offers unparalleled simplicity and diversity, unlocking a vast space of reproducible styles from minimal input. Extensive experiments validate that CoTyle effectively turns a numerical code into a style controller, demonstrating a style is worth one code.
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            <a href="https://www.alphaxiv.org/abs/2511.10484v1" target="_blank" rel="noopener noreferrer">
                胰腺表面分叶性作为CT生物标志物在2型糖尿病机会性筛查中的效用
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            Utility of Pancreas Surface Lobularity as a CT Biomarker for Opportunistic Screening of Type 2 Diabetes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tejas Sudharshan Mathai, Anisa V. Prasad, Xinya Wang, Praveen T. S. Balamuralikr...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像和糖尿病筛查，属于明确的医学领域应用。标题内容涉及CT生物标志物和疾病诊断，与推荐系统、搜索、广告或LLM技术没有任何关联，完全超出了当前关注的技术范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 16:51:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10484v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10484v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disease that affects millions of people worldwide. Early detection is crucial as it can alter pancreas function through morphological changes and increased deposition of ectopic fat, eventually leading to organ damage. While studies have shown an association between T2DM and pancreas volume and fat content, the role of increased pancreatic surface lobularity (PSL) in patients with T2DM has not been fully investigated. In this pilot work, we propose a fully automated approach to delineate the pancreas and other abdominal structures, derive CT imaging biomarkers, and opportunistically screen for T2DM. Four deep learning-based models were used to segment the pancreas in an internal dataset of 584 patients (297 males, 437 non-diabetic, age: 45$\pm$15 years). PSL was automatically detected and it was higher for diabetic patients (p=0.01) at 4.26 $\pm$ 8.32 compared to 3.19 $\pm$ 3.62 for non-diabetic patients. The PancAP model achieved the highest Dice score of 0.79 $\pm$ 0.17 and lowest ASSD error of 1.94 $\pm$ 2.63 mm (p$<$0.05). For predicting T2DM, a multivariate model trained with CT biomarkers attained 0.90 AUC, 66.7\% sensitivity, and 91.9\% specificity. Our results suggest that PSL is useful for T2DM screening and could potentially help predict the early onset of T2DM.
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            <a href="https://www.alphaxiv.org/abs/2511.10412v1" target="_blank" rel="noopener noreferrer">
                3DFETUS：三维超声中胎儿面部平面的标准化
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            3DFETUS: Standardizing Fetal Facial Planes in 3D Ultrasound
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alomar Antonia, Rubio Ricardo, Albaiges Gerard, Salort-Benejam Laura, Caminal Ju...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学影像领域的三维超声胎儿面部分析，属于明确的医学应用范畴。根据用户指定的无关主题，医学、生物学等特定领域应用应被排除，且该研究在标题中未显示出与推荐系统、搜索或广告的任何潜在技术关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 15:33:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10412v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10412v1
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                    Acquiring standard facial planes during routine fetal ultrasound (US) examinations is often challenging due to fetal movement, variability in orientation, and operator-dependent expertise. These factors contribute to inconsistencies, increased examination time, and potential diagnostic bias. To address these challenges in the context of facial assessment, we present: 1) GT++, a robust algorithm that estimates standard facial planes from 3D US volumes using annotated anatomical landmarks; and 2) 3DFETUS, a deep learning model that automates and standardizes their localization in 3D fetal US volumes. We evaluated our methods both qualitatively, through expert clinical review, and quantitatively. The proposed approach achieved a mean translation error of 4.13 mm and a mean rotation error of 7.93 degrees per plane, outperforming other state-of-the-art methods on 3D US volumes. Clinical assessments further confirmed the effectiveness of both GT++ and 3DFETUS, demonstrating statistically significant improvements in plane estimation accuracy.
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            <a href="https://www.alphaxiv.org/abs/2511.10173v1" target="_blank" rel="noopener noreferrer">
                CephRes-MHNet：一种用于精确鲁棒头影测量标志点检测的多头残差网络
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            CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ahmed Jaheen, Islam Hassan, Mohanad Abouserie, Abdelaty Rehab, Adham Elasfar, Kn...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的头影测量标志点检测，属于医学/生物学特定应用范畴。虽然涉及神经网络架构，但其应用场景与推荐系统、搜索或广告完全无关，且没有显示出任何在这些领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 10:37:07
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                <a href="https://arxiv.org/abs/2511.10173v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10173v1
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                    Accurate localization of cephalometric landmarks from 2D lateral skull X-rays is vital for orthodontic diagnosis and treatment. Manual annotation is time-consuming and error-prone, whereas automated approaches often struggle with low contrast and anatomical complexity. This paper introduces CephRes-MHNet, a multi-head residual convolutional network for robust and efficient cephalometric landmark detection. The architecture integrates residual encoding, dual-attention mechanisms, and multi-head decoders to enhance contextual reasoning and anatomical precision. Trained on the Aariz Cephalometric dataset of 1,000 radiographs, CephRes-MHNet achieved a mean radial error (MRE) of 1.23 mm and a success detection rate (SDR) @ 2.0 mm of 85.5%, outperforming all evaluated models. In particular, it exceeded the strongest baseline, the attention-driven AFPF-Net (MRE = 1.25 mm, SDR @ 2.0 mm = 84.1%), while using less than 25% of its parameters. These results demonstrate that CephRes-MHNet attains state-of-the-art accuracy through architectural efficiency, providing a practical solution for real-world orthodontic analysis.
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            <a href="https://www.alphaxiv.org/abs/2511.10136v1" target="_blank" rel="noopener noreferrer">
                正确的视觉，错误的原因：文本到图像生成中的组合保真度
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            Right Looks, Wrong Reasons: Compositional Fidelity in Text-to-Image Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mayank Vatsa, Aparna Bharati, Richa Singh
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本到图像生成的组合保真度问题，这属于纯粹的AIGC和内容生成领域。虽然文本到图像技术本身是LLM相关技术，但论文关注的是视觉生成质量评估，与搜索、推荐或广告中的排序、匹配或用户建模等核心问题没有直接关联。该研究缺乏在RecSys/Search/Ads领域的明确应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 09:49:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10136v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10136v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    The architectural blueprint of today's leading text-to-image models contains a fundamental flaw: an inability to handle logical composition. This survey investigates this breakdown across three core primitives-negation, counting, and spatial relations. Our analysis reveals a dramatic performance collapse: models that are accurate on single primitives fail precipitously when these are combined, exposing severe interference. We trace this failure to three key factors. First, training data show a near-total absence of explicit negations. Second, continuous attention architectures are fundamentally unsuitable for discrete logic. Third, evaluation metrics reward visual plausibility over constraint satisfaction. By analyzing recent benchmarks and methods, we show that current solutions and simple scaling cannot bridge this gap. Achieving genuine compositionality, we conclude, will require fundamental advances in representation and reasoning rather than incremental adjustments to existing architectures.
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            <a href="https://www.alphaxiv.org/abs/2511.10055v1" target="_blank" rel="noopener noreferrer">
                基于HCM-GRPO的图像美学推理：赋能紧凑模型实现卓越性能
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            Image Aesthetic Reasoning via HCM-GRPO: Empowering Compact Model for Superior Performance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiyuan Hu, Zheng Sun, Yi Wei, Long Yu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像美学评估这一纯粹的计算机视觉任务，未涉及推荐系统、搜索或广告领域的任何技术要素。标题中提到的紧凑模型优化和性能提升方法（HCM-GRPO）缺乏在RecSys/Search/Ads场景中的潜在应用路径，属于视觉质量评估的特定领域研究。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 07:57:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.10055v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.10055v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The performance of image generation has been significantly improved in recent years. However, the study of image screening is rare and its performance with Multimodal Large Language Models (MLLMs) is unsatisfactory due to the lack of data and the weak image aesthetic reasoning ability in MLLMs. In this work, we propose a complete solution to address these problems in terms of data and methodology. For data, we collect a comprehensive image screening dataset with over 128k samples, about 640k images. Each sample consists of an original image, four generated images. The dataset evaluates the image aesthetic reasoning ability under four aspects: appearance deformation, physical shadow, placement layout, and extension rationality. Regarding data annotation, we investigate multiple approaches, including purely manual, fully automated, and answer-driven annotations, to acquire high-quality chains of thought (CoT) data in the most cost-effective manner. Methodologically, we introduce a Hard Cases Mining (HCM) strategy with a Dynamic Proportional Accuracy (DPA) reward into the Group Relative Policy Optimization (GRPO) framework, called HCM-GRPO. This enhanced method demonstrates superior image aesthetic reasoning capabilities compared to the original GRPO. Our experimental results reveal that even state-of-the-art closed-source MLLMs, such as GPT4o and Qwen-VL-Max, exhibit performance akin to random guessing in image aesthetic reasoning. In contrast, by leveraging the HCM-GRPO, we are able to surpass the scores of both large-scale open-source and leading closed-source models with a much smaller model.
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            <a href="https://www.alphaxiv.org/abs/2511.09977v1" target="_blank" rel="noopener noreferrer">
                STELLAR：面向低资源语言和真实世界数据的场景文本编辑器
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            STELLAR: Scene Text Editor for Low-Resource Languages and Real-World Data
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongdeuk Seo, Hyun-seok Min, Sungchul Choi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于场景文本编辑，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及低资源语言处理，但主要应用于图像中的文本编辑，没有明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 05:18:03
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                <a href="https://arxiv.org/abs/2511.09977v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09977v1
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                    Scene Text Editing (STE) is the task of modifying text content in an image while preserving its visual style, such as font, color, and background. While recent diffusion-based approaches have shown improvements in visual quality, key limitations remain: lack of support for low-resource languages, domain gap between synthetic and real data, and the absence of appropriate metrics for evaluating text style preservation. To address these challenges, we propose STELLAR (Scene Text Editor for Low-resource LAnguages and Real-world data). STELLAR enables reliable multilingual editing through a language-adaptive glyph encoder and a multi-stage training strategy that first pre-trains on synthetic data and then fine-tunes on real images. We also construct a new dataset, STIPLAR(Scene Text Image Pairs of Low-resource lAnguages and Real-world data), for training and evaluation. Furthermore, we propose Text Appearance Similarity (TAS), a novel metric that assesses style preservation by independently measuring font, color, and background similarity, enabling robust evaluation even without ground truth. Experimental results demonstrate that STELLAR outperforms state-of-the-art models in visual consistency and recognition accuracy, achieving an average TAS improvement of 2.2% across languages over the baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.09965v1" target="_blank" rel="noopener noreferrer">
                基于等变性采样的扩散模型图像修复改进
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            Equivariant Sampling for Improving Diffusion Model-based Image Restoration
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenxu Wu, Qingpeng Kong, Peiang Zhao, Wendi Yang, Wenxin Ma, Fenghe Tang, Zihan...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像修复领域的扩散模型改进，属于纯粹的计算机视觉应用。虽然提到了扩散模型，但论文内容限定在图像处理任务，没有展示与推荐系统、搜索或广告的潜在关联。等变性采样技术在该上下文中是针对视觉数据特性设计的，无法直接应用于文本或序列数据的推荐/搜索场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-13 04:56:53
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                <a href="https://arxiv.org/abs/2511.09965v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.09965v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in generative models, especially diffusion models, have significantly improved image restoration (IR) performance. However, existing problem-agnostic diffusion model-based image restoration (DMIR) methods face challenges in fully leveraging diffusion priors, resulting in suboptimal performance. In this paper, we address the limitations of current problem-agnostic DMIR methods by analyzing their sampling process and providing effective solutions. We introduce EquS, a DMIR method that imposes equivariant information through dual sampling trajectories. To further boost EquS, we propose the Timestep-Aware Schedule (TAS) and introduce EquS$^+$. TAS prioritizes deterministic steps to enhance certainty and sampling efficiency. Extensive experiments on benchmarks demonstrate that our method is compatible with previous problem-agnostic DMIR methods and significantly boosts their performance without increasing computational costs. Our code is available at https://github.com/FouierL/EquS.
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                divider.className = 'papers-divider';
                
                const dividerLabel = document.createElement('div');
                dividerLabel.className = 'papers-divider-label';
                dividerLabel.textContent = '点击展开更多论文（评分较低）';
                dividerLabel.addEventListener('click', function() {
                    papersContainer.classList.toggle('expanded-level-2');
                    this.textContent = papersContainer.classList.contains('expanded-level-2') ? 
                        '点击收起低分论文' : '点击展开更多论文（评分较低）';
                });
                
                divider.appendChild(dividerLabel);
                
                // 在所有非精选论文的最后一个元素后面添加分割线
                const normalPapers = papersContainer.querySelectorAll('.simple-paper-card');
                if (normalPapers.length > 0) {
                    const lastNormalPaper = normalPapers[normalPapers.length - 1];
                    papersContainer.insertBefore(divider, lastNormalPaper.nextSibling);
                }
            }
            
            // 为每个非精选论文添加点击标题展开/折叠详情的功能
            const collapsedPapers = document.querySelectorAll('.collapsed-level-1');
            collapsedPapers.forEach(paper => {
                const titleElement = paper.querySelector('h3');
                if (titleElement) {
                    titleElement.style.cursor = 'pointer';
                    
                    // 创建展开/折叠图标元素并设置样式
                    const iconElement = document.createElement('i');
                    iconElement.className = 'expand-icon fa fa-eye-slash cursor-pointer';
                    iconElement.style.marginRight = '8px';
                    
                    // 将图标插入到标题链接之前，作为同级元素
                    const linkElement = titleElement.querySelector('a');
                    if (linkElement) {
                        // 将图标直接添加到标题元素中，位于链接之前
                        titleElement.insertBefore(iconElement, linkElement);
                        
                        // 为图标单独添加点击事件处理展开/折叠
                        iconElement.addEventListener('click', function(e) {
                            e.stopPropagation(); // 阻止事件冒泡到标题元素
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                this.className = isExpanded ? 
                                    'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                this.style.marginRight = '8px';
                            }
                        });
                    }
                    
                    // 为标题元素添加点击事件，也可以展开/折叠，但会检查点击目标
                    titleElement.addEventListener('click', function(e) {
                        // 仅当点击的是标题本身（非链接、非图标）时才展开/折叠
                        if (!e.target.closest('a') && !e.target.closest('.expand-icon')) {
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                const iconElement = this.querySelector('.expand-icon');
                                if (iconElement) {
                                    iconElement.className = isExpanded ? 
                                        'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                    iconElement.style.marginRight = '8px';
                                }
                            }
                        }
                    });
                }
            });
            
            // 实现"仅显示精选"按钮功能
            const showSelectedButton = document.getElementById('show-selected');
            if (showSelectedButton) {
                showSelectedButton.addEventListener('click', function() {
                    // 显示所有精选论文，隐藏所有普通论文
                    const selectedPapers = document.querySelectorAll('.paper-card');
                    const normalPapers = document.querySelectorAll('.simple-paper-card');
                    
                    selectedPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    normalPapers.forEach(paper => {
                        paper.style.display = 'none';
                    });
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${selectedPapers.length} 篇论文 (共 ${selectedPapers.length + normalPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-all').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 隐藏展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) expandToggle.style.display = 'none';
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'none';
                });
            }
            
            // 实现"全部论文"按钮功能
            const showAllButton = document.getElementById('show-all');
            if (showAllButton) {
                showAllButton.addEventListener('click', function() {
                    // 显示所有论文
                    const allPapers = document.querySelectorAll('.paper-card, .simple-paper-card');
                    allPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    // 重置折叠状态
                    papersContainer.classList.remove('expanded-all');
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${allPapers.length} 篇论文 (共 ${allPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-selected').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 重新显示展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) {
                        expandToggle.style.display = 'block';
                        expandToggle.textContent = '展开全部非精选论文';
                    }
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'block';
                });
            }
        });
    </script>
    <script>
    
    // 初始化日历
    document.addEventListener('DOMContentLoaded', () => {
        try {
            console.log('Attempting to initialize calendar...');
            initCalendar();
        } catch (error) {
            console.error('Error initializing calendar:', error);
        }
    });
    
    // 日历初始化函数
    function initCalendar() {
        const toggleBtn = document.getElementById('date-picker-toggle');
        const datePicker = document.getElementById('date-picker');
        const calendarGrid = document.getElementById('calendar-grid');
        const prevMonthBtn = document.getElementById('prev-month');
        const nextMonthBtn = document.getElementById('next-month');
        const currentMonthEl = document.getElementById('current-month');
        const selectedDateText = document.getElementById('selected-date-text');
        
        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251105","20251107","20251009","20251113","20251030","20251111","20251031","20251017","20251021","20251010","20251024","20251022","20251029","20251114","20251016","20251015","20251028","20251014","20251112","20251106","20251023"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>